Special Populations: A Challenge to Juvenile Justice [WLOs: 1, 2, 3] [CLOs: 1, 2, 3, 4, 5, 6]

Consider the goals of the juvenile justice system, which focus on reintegrating juveniles into the community as productive members of society. Prior to beginning work on this discussion, read Chapters 8 and 9 of Introduction to Juvenile Justice. (PROVIDED IN ATTACHMENTS) In addition,

ALL ARTICLES PROVIDED IN ATTACHMENTS!!!MUST USE AS RESOURCES!!

• Read Youth Pathways to Placement: The Influence of Gender, Mental Health Needs and Trauma on Confinement in the Juvenile Justice System
• Read Treatment Services in the Juvenile Justice System: Examining the Use and Funding of Services by Youth on Probation
• Read The Impact of Victimization and Mental Health Symptoms on Recidivism for Early System-Involved Juvenile Offenders
• Read Research Review: Independent Living Programmes: The Influence on Youth Ageing out of Care
• Watch Juvenile Justice.  https://fod.infobase.com/PortalPlaylists.aspx?wID=100753&xtid=56083 

You are also encouraged to review the

Week 4 Recommended Resources

.

Compare and contrast treatment options for special populations identified in our text (i.e., early starters, juvenile gangs, or juvenile sex offenders) and advocate for, or against, shifting juveniles in this category to treatment options outside normal juvenile delinquency programs. You should identify a specific category identified as being part of special populations and a treatment option as part of the discussion. What are the benefits to this program in addressing the special population? Are there drawbacks, if so what are they? How are outcomes identified and measured?

Note: this discussion format will differ from formats in prior courses. The goal of this discussion forum is to have a single conversation about the topic of treatment for special populations of juvenile offenders, not a series of separate conversations.

E M P I R I C A L R E S E A R C H

Youth Pathways to Placement: The Influence of Gender, Mental
Health Need and Trauma on Confinement in the Juvenile
Justice System

Erin M. Espinosa • Jon R. Sorensen •

Molly A. Lopez

Received: 9 April 2013 / Accepted: 27 June 2013 / Published online: 4 July 2013

� Springer Science+Business Media New York 2013

Abstract Although the juvenile crime rate has generally

declined, the involvement of girls in the juvenile justice

system has been increasing. Possible explanations for this

gender difference include the impact of exposure to trauma

and mental health needs on developmental pathways and

the resulting influence of youth’s involvement in the justice

system. This study examined the influence of gender,

mental health needs and trauma on the risk of out-of-home

placement for juvenile offenders. The sample included

youth referred to three urban juvenile probation depart-

ments in Texas between January 1, 2007 and December 31,

2008 and who received state-mandated mental health

screening (N = 34,222; 30.1 % female). The analysis

revealed that, for both genders, elevated scores on the

seven factor-analytically derived subscales of a mental

health screening instrument (Alcohol and Drug Use,

Depressed-Anxious, Somatic Complaints, Suicidal Idea-

tion, Thought Disturbance, and Traumatic Experiences),

especially related to past traumatic experiences, influenced

how deeply juveniles penetrated the system. The findings

suggest that additional research is needed to determine the

effectiveness of trauma interventions and the implemen-

tation of trauma informed systems for youth involved with

the juvenile justice system.

Keywords Detention � Incarceration, disposition �
Gender disparity � Trauma � Mental health

Introduction

Adolescence is a period of developmental transition char-

acterized by changes in family, school, peers, self-concept,

and general physical development (Bergman and Scott

2001). Although most youth navigate this developmental

period successfully, incidents of rule breaking and behav-

ioral problems are common and can result in involvement

with law enforcement. Some research suggests that inter-

vention by the criminal justice system during the critical

period of adolescence may negatively impact youth out-

comes, including decreasing opportunities for meeting

educational goals and increasing the risk for later

involvement in delinquency and deviance (Sampson and

Laub 2005; pipeline articles). Recent trends have shown a

steady decline in juvenile offending overall, particularly

among violent crimes. However, statistics have also shown

a trend toward increased delinquency in females. For

example, Snyder (2008) reported that between 1994 and

2006, arrests for simple assault declined by 4 % for boys

while the rate increased by 19 % for girls. Given the

gender differences in adolescent development, it seems

critical to examine the pathways that lead to youth

involvement in the juvenile justice system through this

lens.

Research consistently shows gender-related differences

in delinquent behavior. The literature suggests these

E. M. Espinosa (&) �

M. A. Lopez

Texas Institute for Excellence in Mental Health, Center

for Social Work Research, School of Social Work,

The University of Texas at Austin, 1717 West 6th Street,

Ste. 335, Austin, TX 78703, USA

e-mail: erin.espinosa@austin.utexas.edu

M. A. Lopez

e-mail: mlopez@austin.utexas.edu

J. R. Sorensen

Department of Criminal Justice, East Carolina University,

Rivers Building, Office #245, Mail Stop 505, Greenville,

NC 27858, USA

e-mail: sorensenj@ecu.edu

123

J Youth Adolescence (2013) 42:1824–1836

DOI 10.1007/s10964-013-9981-x

differences first emerge early in child development and

become more pervasive in adolescence. Some leaders in

criminology have suggested that gender differences in

delinquent behavior can be attributed to differential

socialization between genders (Bottcher 2001), while oth-

ers have argued that differences are tied to offender status

in a gender-stratified society (Chesney-Lind 2002). How-

ever, a third model emerges when examining both the

developmental criminological and the developmental psy-

chology literature. The developmental psychology litera-

ture has shown that females are more likely to exhibit

internalizing symptoms that may not come to the attention

of the adults in their life (Rosenfield et al. 2005), while

males are more likely to exhibit externalizing behaviors,

which are problematic for other people and society

(Compton et al. 2002; Kazdin 2005). Greater internalizing

results in girls having increased rates of depression, bipo-

lar, anxiety, post-traumatic stress, and other mood disor-

ders. Boys tend to have higher rates of conditions such as

attention-deficit/hyperactivity disorder, oppositional defi-

ant disorder, and conduct disorder. Therefore, one possible

explanation of the gender differences found in the

involvement of youth in the juvenile justice system could

be explained by differences in mental health conditions that

may develop and/or intensify in adolescence.

Gendered Pathways to Delinquency

Pathways toward and through the juvenile justice system

differ between girls and their male counterparts. This may

be related to how boys and girls develop their self-concepts

and identities. Boys’ self-concepts and identities are

developed in relationship to the world, while girls’ and

young women’s self-concepts and identities are developed

through their interactions with others (Gilligan and Brown

1992). Gilligan and Brown contend that female moral

development is based on a personal view and commitment

to others. Although female offenders occasionally engage

in conduct more stereotypical of males, such as aggression

and assaultive behavior, more often they suppress their

aggression and struggle with the difficulty of managing

their emotions, especially those associated with depression

and anxiety (Ford et al. 2006). Delinquent girls have a

higher risk of self-devaluation, suicidality (Wasserman

et al. 2005), and conflict with family and school compared

with their male counterparts (Zoccolillo et al. 1996).

Attachment, interdependence and connectedness are criti-

cal to the foundation of their identity.

Gender Disparity in System Processing

Studies of delinquency and the response of the juvenile

justice system have consistently found both legal and extra-

legal factors contribute to the detention and dispositional

outcomes of youth involved in juvenile offending. How-

ever, findings have been inconsistent regarding the effects

of gender on case outcomes in post-adjudication disposi-

tion decisions (Belknap and Holsinger 2006). Some studies

have revealed girls were the recipients of more severe

sanctions than their male counterparts, especially in

response to status offenses (Chesney-Lind 2002). Other

studies indicated females received more lenient outcomes

for delinquent behavior (class B misdemeanor and higher

offenses) than males. Some research discovered that out-

comes depend on the stage of processing. For instance,

MacDonald and Chesney-Lind (2001) reported no differ-

ence between boys and girls in the decision to petition an

offense. However, during the adjudication stage, ‘‘charge

seriousness’’ was more important for girls than boys with

the reverse trend during the disposition stage. Thus, when

female juvenile offenders were adjudicated delinquent,

they were ‘‘more likely than boys to be given a restrictive

sanction for a less serious offense’’ (p. 187).

It has become common knowledge in criminology that

by engaging in a practice referred to as bootstrapping,

courts detain females through findings of contempt of

court, probation violations, or violations of court orders for

underlying status offenses or minor delinquent behavior

(Sherman 2005). As a result of bootstrapping, early evi-

dence suggests more female juvenile offenders are detained

prior to adjudication for offenses less threatening to the

community than those of their male counterparts. Data

from the Juvenile Detention Alternatives Initiative (JDAI),

launched by the Annie E. Casey Foundation in 1992,

demonstrated the number of juveniles housed in secure

detention nationwide increased by 72 % between 1985 and

1995 (Sherman 2005). While it may be assumed this

increase reflected the need for community safety, less than

one-third of the juvenile offenders detained in 1995 were

charged with a violent offense. Across both genders, more

youth were detained for status offenses than violent

offenses, with violations of court orders accounting for

39.9 % of the detention population. This trend was even

greater for female juvenile offenders, who were more likely

than their male counterparts to be detained for status

offenses and technical violations (Sherman 2005). Similar

findings have been demonstrated in several study replica-

tions (American Bar Association and the National Bar

Association 2001; Sickmund et al. 2004).

For instance, Gavazzi et al. (2006) noted girls were more

likely to be detained for incorrigibility and domestic vio-

lence, and parents were more likely to be the complainants.

Their findings also indicated boys were more likely to be

arrested for property offenses, with complainants more

likely to be community citizens. The authors summarize

the difference between male and female juvenile detention

J Youth Adolescence (2013) 42:1824–1836 1825

123

decisions by stating that: ‘‘boys are detained as a response

to public safety issues, whereas girls are detained because

of problems at home’’ (p. 608). By 2003, this trend had

extended to custodial placements other than detention as

well, with females accounting for 40 % of the status

offenders but only 14 % of delinquents held in custody

(Snyder and Sickmund 2006).

Mental Health Disorders and Delinquency

Recent studies suggest a correlation between juvenile jus-

tice system processing and psychiatric disorders, with some

research indicating girls with mental health needs are

funneled deeper into the system for less serious offenses

than their male counterparts. Abram et al. (2003), in a

study of Cook County Juvenile Detention youth, found

females were 1.4 times more likely than males to meet

diagnostic criteria for at least one disorder, and they also

were more likely to have at least one co-morbid dis

order.

Davis et al. (2009) discovered females receiving care in the

community mental health system were arrested at younger

ages and more frequently than girls not receiving public

mental health treatment. In addition, for those youth

needing hospitalizations, girls had shorter lengths of stay

than boys (Pavkov et al. 1997). These findings have lead

some researchers to suggest girls are typically undertreated

for their mental health needs and others to suggest this lack

of treatment results in their involvement in the juvenile

justice system (Wasserman

et al. 2005).

Youth involved with the juvenile justice system often

have not one, but several co-morbid psychiatric disorders.

Wasserman et al. (2005) found the prevalence of youth

meeting criteria for at least one psychiatric disorder to be

39 %, with 16 % meeting criteria for three or more disor-

ders. In addition to the growing prevalence of youth with

mental health challenges in the juvenile justice system,

studies also indicate mental health disorders are correlated

with delinquent behavior. Several prospective studies

indicate hyperactivity (Lynam et al. 2000), conduct disor-

ders and emotional disorders (Copeland et al. 2007; Boots

2008; Boots and Wareham 2009) serve as key indicators

for involvement with the justice system. Specifically, Co-

peland et al. (2007) found 20.6 % of female juvenile

offending and 15.3 % of male juvenile offending was

attributable to mental health disorders, after controlling for

offense level and poverty. Among specific psychiatric

profiles, the findings indicate co-occurring anxiety and

depressive disorders had the strongest association with

delinquent behavior. Boots and Wareham (2009) extended

these findings further when they demonstrated a moderate

correlation between depression and anxiety (r = .577) and

future offending.

Trauma and Delinquency

Although not all youth who experience trauma engage in

delinquent activity, studies of youth involved with the

juvenile justice system have found high rates of trau-

matic experiences, generally between 70 and 90 %

(McMackin et al. 1998; Steiner et al. 1997). Some

studies have found boys and girls involved with the

juvenile justice system experienced different types of

traumas, with males more likely to have witnessed a

violent event and females more likely to have been the

victim of violence. The Survey of Youth in Residential

Placement, conducted on a sample of over 7,000 incar-

cerated youth, indicated females were almost twice as

likely to report prior physical abuse (42 % of females

versus 22 % of males), and females reported a higher

likelihood (69 % of females versus 40 % of their male

counterparts) of the perpetrator of the physical abuse

being a sibling or mother (Sedlak and McPherson 2010).

Researchers also found girls who reside in violent homes

have heightened risk factors for engaging in delinquent

activity, such as truancy, sexual promiscuity, running

away, and substance abuse (Thornberry et al. 2004). Not

surprisingly, female juveniles arrested for running away

frequently report experiences of family violence and

emotional, physical, and sexual abuse and report these

conditions as their primary motivation for leaving home

(Chesney-Lind 2002). Furthermore, studies indicate

females who have experienced trauma develop mental

health problems as a result of that trauma more often

than their male counterparts (Crimmins et al. 2000).

Some studies found girls who have mood disorders are

more likely to have experienced trauma and are more

likely to have post-traumatic stress disorder (Wasserman

et al. 2005).

Sexual victimization, in particular, is a common form

of trauma experienced by girls involved in the justice

system and is likely a contributing factor to the complex

mental health needs of this population. Although virtually

absent from formal theories of female delinquency, some

studies examined the correlation between sexual abuse

and female juvenile delinquency. In a study of chroni-

cally delinquent offenders, Sherman (2005) found 77 %

of female offenders had a history of sexual abuse in

comparison to only 3 % of the males, suggesting a

potential relationship between trauma and chronic delin-

quency in girls. Furthermore, Goodkind et al. (2006)

found juvenile justice-involved girls who have experi-

enced some form of sexual abuse had poorer mental

health and more substance use, risky sexual behavior, and

delinquent behavior than those who had not experienced

this form of trauma.

1826 J Youth Adolescence (2013) 42:1824–1836

123

Hypotheses

Despite the overall decline in the rate of juvenile delin-

quency, the involvement of girls with the juvenile justice

system has increased. In addition, there has been an

enhanced recognition of the disproportionate representation

of youth with mental health needs and trauma histories in

the juvenile justice system. While males typically are

associated with externalizing problems, females dispro-

portionately are identified with internalizing problems and

interpersonal aggression. This changing demographic of

juvenile delinquency poses challenges to the traditional

juvenile justice system accustomed to handling behaviors

associated with externalizing manifestations of delin-

quency. Analyzing the extent to which mental health need,

trauma, and gender influence juvenile justice system pro-

cessing will provide a more comprehensive understanding

of the pathways youth take in the juvenile justice system,

as well as identify potential modifications needed to

address the unique needs of youth accessing the system in

the future.

First, we hypothesized that more girls would be placed

outside of the home for bootstrap level offenses, such as

status offenses and violation of probation, than boys. This

analysis sought to determine whether girls are funneled

deeper into the system for lower level offenses than their

male counterparts. Second, we hypothesized that greater

mental health need, as measured by the mental health

screening instrument, would be associated with a greater

risk of out-of-home placement. Finally, we formed an

exploratory hypothesis aimed toward examining the influ-

ence of the endorsement of prior traumatic experiences, as

measured by the mental health screening instrument, on the

restrictiveness of out-of-home placement decisions. We

were interested in whether a trauma history increased the

likelihood of a juvenile being removed from their home. In

addition to these three primary hypotheses, a secondary

analysis explored the relative importance of these variables

on different types of out-of-home placement.

Method

The study sample included all youth referred to three urban

juvenile probation departments in Texas during the period

of January 1, 2007 through December 31, 2008. Only youth

who received the state-mandated mental health screening,

the Massachusetts Youth Screening Instrument-Second

Version (MAYSI-2) were included (N = 34,222; 30.1 %

female). This secondary dataset included all demographic,

offense, disposition and placement data collected by

trained juvenile probation officers and clinicians within the

departments. Data was obtained with approval from the

Chief Juvenile Probation Officer and the juvenile board and

the protocol was reviewed and approved by the Institu-

tional Review Board at the researchers’ university.

Measures

The main predictor variables considered were referral

offense seriousness, gender, and level of mental health

need. Gender is a dichotomous static variable and was

coded as male (0) or female (1) for analysis. The referral

offense seriousness and level of mental health need vari-

ables could be interpreted with a broad array of values;

therefore, specific operational definitions and categorical

values for these variables were developed prior to con-

ducting analyses.

Offense Seriousness

Youth could have been referred to local juvenile probation

departments for multiple offenses on any one referral

event. Therefore, this study targeted the referral offense

associated with most severe disposition during the sample

period. The categorical coding guidelines identified within

the Texas Juvenile Probation Commission (TJPC) data

codebook were used for establishing operational definitions

and assigning categorical values for offense seriousness.

The TJPC data codebook is used by juvenile probation

officers collecting and entering data into the state’s data

collection system. This coding process categorized the

4,019 types of offenses into a continuous classification

variable ranging from the least serious 1 (status offenses) to

the most severe 8 (capital felony). The classifications

between status offense and capital felony included the

following: 2 = Class B misdemeanor; 3 = Class A mis-

demeanor; 4 = State-jail felony; 5 = Third-degree felony;

6 = Second-degree felony; and 7 = First-degree felony.

In addition to the ordinal offense severity code, two

dichotomous indicator variables were constructed to eval-

uate the potential bootstrapping of juveniles into and

through the juvenile justice system. Bootstrapping has been

defined in the literature as engaging in a practice whereby

courts detain females through findings of contempt of
court, probation violations, or violations of court orders for
underlying status offenses or minor delinquent behavior

(Sherman 2005). A traditional bootstrap variable (Status)

included offenses that have been typically categorized as

status offenses (otherwise known as ‘‘Conduct Indicating

Need for Supervision Offenses’’ or CHINS offenses), Class

C misdemeanors, and contempt of court referrals. These

types of offenses include runaway, truancy, and curfew

violations. Class C misdemeanors are typically violations

of city or county ordinances and are processed in a manner

similar to status offenses. A second bootstrap variable

J Youth Adolescence (2013) 42:1824–1836 1827

123

(VOP) included violations of probation or juvenile court

order.

Mental Health Need

Texas has adopted the MAYSI-2 for mental health

screening within the juvenile justice system (Grisso 2004;

Schwank et al. 2003). The MAYSI-2 is a 52-item, self-

report screening instrument completed by youth between

the ages of 12 and 17 upon intake in the juvenile justice

system. The MAYSI-2 contains seven factor-analytically

derived subscales: Alcohol and Drug Use, Angry-Irritable,

Depressed-Anxious, Somatic Complaints, Suicidal Idea-

tion, Thought Disturbance, and Traumatic Experiences.

Studies have demonstrated good concurrent validity when

comparing MAYSI-2 scales with scores on other mental

health measures (Archer et al. 2004; Grisso and Barnum

2006). Test–retest reliability up to eight days later was

moderate to good, ranging from 0.53 to 0.89 (Grisso and

Barnum 2006). Cut-off scores for the MAYSI-2 subscales

(excluding Traumatic Experiences) were developed to

identify youth scoring greater than 90 % of the normative

sample on each subscale (Grisso and Barnum 2006).

Overall mental health need was defined as the total number

of subscales reaching this ‘‘warning’’ cut-off, ranging from

0 to 6.

The traumatic experiences subscale of the MAYSI-2

does not have established warning cut-offs, and so was kept

in its original reporting format, with a scoring range of 0–5.

Four questions on the subscale are common to both gen-

ders: ‘‘Have you been badly hurt or been in danger of

getting badly hurt or killed?’’, ‘‘Have you ever in your

whole life had something very bad or terrifying happen to

you?’’, ‘‘Have you ever seen someone severely injured or

killed?’’, and ‘‘Have you had a lot of bad thoughts or

dreams about a bad or scary event that happened to you?’’.

For boys, the fifth question is ‘‘Have people talked about

you a lot when you’re not there?’’. For girls, this fifth

question is ‘‘Have you ever been raped or been in danger of

getting raped?’’.

Level of Placement

Level of placement was categorized into a five-point

ordinal variable, with higher scores on the scale repre-

senting more ‘‘severe’’ placements. Categorization reflec-

ted not only the determination of whether the facility was

secure or non-secure, but also consideration of what

intercept point in the juvenile justice system (pre or post

disposition) the juvenile could be placed within the facility.

No placement is reflected by a ‘‘0’’ on the scale and

detention is reflected by a ‘‘1’’. Although often a secure

setting, juvenile detention facilities are the first type of

facility within the juvenile justice system a youth can be

placed and are frequently used to hold juveniles while

awaiting court decisions pre-adjudication. The next three

levels of placement severity within the composite were

non-secure (2), county secure (3), and state correctional

facility (4). Non-secure facilities included facilities

licensed by the state child welfare agency to provide foster

care or treatment services and included residential treat-

ment centers, emergency shelters, substance abuse treat-

ment facilities, therapeutic camps, and foster care. Local

juvenile probation departments contract with non-secure

facilities to care for juveniles who are considered low risk

and in need of some form of treatment or other basic care

needs. County secure facilities included county-operated

secure post-adjudication programs registered with the

state’s juvenile justice department to serve as an interme-

diate placement option for moderate or high-risk juveniles.

These facilities may include juveniles who have been

adjudicated of either misdemeanor or felony offenses. State

correctional facilities included high security facilities

intended for high-risk juveniles with felony adjudications

and represent the state’s youth prison system (Texas

Juvenile Justice Department 2012).

Control Variables

Control variables included age at first referral, age at target

referral, ethnicity, severity of offense history and prior

probation referrals. Ethnicity was reflected by dummy

coding two variables—Hispanic (1) or White (0) and Black

(1) or White (0). Both age at first referral and age at target

referral were also included in the analyses. Almost half of

the juveniles in the sample had a prior record with the

partnering juvenile probation departments (n = 16,077).

Severity of offense history was categorized using the same

procedures as the target referral offense. The seriousness of

offense history ranged from 0, indicating no prior record, to

8, indicating prior referral for a capital murder. Prior pro-

bation referrals were defined as the number of prior dis-

positions to probation supervision.

Procedures

Differences in variables of interest and control variables by

gender were examined through independent t-tests and Chi

square analyses. Multivariate analyses examined level of

placement by gender and mental health need. First, the

analyses sought to establish the general influence of mental

health need and gender on level of placement. The exam-

ination of the general influence of the predictor variables

on level of placement for all facility types utilized an OLS

regression model with gender (0 = male, 1 = female)

included in the equation, along with other predictor

1828 J Youth Adolescence (2013) 42:1824–1836

123

variables, regressed on facility composite (0 = no place-

ment, 1 = detention, 2 = non-secure, 3 = secure, 4 =

corrections). The OLS regression model is presented for

ease of interpretation. Results from ordinal logistic

regression and probit models confirmed the significance,

direction, and relative magnitude of coefficients presented

in the OLS model.

Additional analyses examined the influence of the pre-

dictor variables by specific facility type and gender at both

the pre-adjudicatory and post-adjudicatory phases, requir-

ing separate female and male models. A dichotomous

outcome variable was created for the pre-adjudicatory

detention decision and coded as either not detained (0) or

detained (1). Binary logistic regression was used to model

the detention decision. Multinomial logistic regression was

used to model post-adjudicatory placement, which includes

separate panels to describe the influence of predictors on

placement in non-secure facilities, county operated secure

facilities, and state correctional facilities. Alternatives to

placement served as the reference category for the multi-

nomial comparisons. Detention was included as a predictor

variable in the post-adjudicatory placement model. Status

offense had to be removed from the post-adjudicatory

placement model. Limited cell size prevented model con-

vergence when status offense was included. Additional

analysis was conducted to test for differences between

regression coefficients of the gendered models. The for-

mula suggested by Brame et al. (1998) was used to test for

differences between the model coefficients:

Z ¼
b1 � b2
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

SEb21 þ SEb22
p

Results

Table 1 presents outcomes, offense seriousness, mental

health need, and control variables by gender. The mean

facility composites show boys are placed deeper in the

system overall. The binary outcomes show males are more

likely to be placed in each type of confinement, with the

exception of non-secure placement, where girls are twice as

likely to be placed. Part of the reason is apparent in the

Table 1 Level of placement,
offense seriousness, mental

health need, and control

variables by gender

* p \ .05; ** p \ .01;
*** p \ .001

Female

Mean (SD)

Male

Mean (SD)

Test of difference

Level of placement

Facility composite 0.496 (0.748) 1.024 (1.212) t = -49.130***

Detention 0.373 0.531 v2 = 725.656***

Non-secure placement 0.068 0.033 v2 = 213.268***

County secure placement 0.013 0.142 v2 = 1293.434***

State correctional commitment 0.008 0.048 v2 = 332.220***

Offense seriousness

Offense composite 2.091 (1.956) 2.789 (1.641) t = -48.709***

Status offense (Boot1) 0.144 0.068 v2 = 512.619***

VOP (Boot2) 0.089 0.143 v2 = 192.406***

Mental health need

Warnings (total #) 0.284 (0.697) 0.232 (0.688) t = 6.400***

Drug 0.023 0.020 v2 = 4.854*

Angry 0.109 0.068 v2 = 166.115***

Depressed 0.104 0.047 v2 = 385.650***

Somatic 0.065 0.036 v2 = 143.961***

Suicide 0.141 0.059 v2 = 628.816***

Thought 0.164 0.113 v2 = 94.904***

Trauma score 1.1300 (1.470) 1.120 (1.232) t = 11.182***

Control variables

Race–Black 0.363 0.373 v2 = 3.386

Ethnicity–Hispanic 0.390 0.413 v2 = 15.993***

Age at referral 14.900 (1.275) 14.956 (1.387) t = -3.581***

Age of onset 14.383 (1.399) 14.140 (1.560) t = 14.225***

Severity of offense history 0.894 (1.508) 1.691 (2.163) t = -39.075***

Prior probated dispositions 0.208 (0.560) 0.455 (0.830) t = -32.108***

Prior facility composite 0.205 (0.536) 0.521 (0.985) t = -38.208***

J Youth Adolescence (2013) 42:1824–1836 1829

123

level of offense, which shows that girls, on average,

commit less serious offenses and are more than twice as

likely as boys to be referred for status offenses. Girls also

had less serious prior records, as evidenced by the control

variables. They also tended to be slightly younger at

referral and older at age of onset than boys.

Consistent with existing literature on prevalence of

mental health disorders and gender, girls had a higher level

of mental health need as evidenced by the total number of

warnings and a greater percentage of girls with need across

all subscales. Trauma scores were also higher for girls than

boys. Girls also tended to be clustered at the higher end of

the scale, with 10.9 % of girls reporting four or more

trauma indicators compared to only 5.2 % of the boys (not

tabled). The percentage of females reporting five or more

trauma indicators was over four times higher (4.1 %) than

their male counterparts (0.9 %).

Analysis of the predictor variables regressed on the level

of placement composite, reported in Table 2, indicated that

all predictor variables were statistically significant. The

Betas from the OLS regression revealed the strongest

predictors of increased severity of out-of-home placement

were higher scores on the MAYSI-2 Trauma scale, offense

severity, age at first referral, and commission of a probation

violation. Gender was negatively related to level of

placement, indicating that being female decreased a juve-

nile’s overall likelihood of being placed at a higher level of

placement. Prior offense and probation history were also

related to increased levels of out-of-home placement, as

was minority status and overall mental health need, while

the commission of a status offense and lower age at target

referral were related to lower levels of placement.

Detention Decision

The logistic regression analysis presented in Table 3 indi-

cates gender differences among predictor variables when

regressed on the variable reflecting whether or not a

juvenile was detained. As the age at target referral for a

male juvenile offenders increased, the odds of being

detained increased. However, for girls, as their age at target

referral increased, their odds of being detained decreased.

Commission of status offenses was negatively related to a

detention decision for both genders, although boys were

slightly less likely to be detained than girls for status

Table 2 Predictor variables regressed on level of placement
composite

B SE Beta

Severity of offense .195 .003 .269***

Gender–female -.152 .010 -.062***

Status offense (Boot1) -.229 .017 -.059***

VOP (Boot2) .709 .018 .211***

Mental health need .043 .007 .067***

Trauma score .039 .004 .275***

Race–Black .181 .012 .078***

Ethnicity–Hispanic .145 .012 .064***

Age at target referral -.056 .006 -.068***

Age at first referral .049 .005 .248***

Prior offense history .137 .004 .046***

Prior probation history .401 .009 .026***

Constant -.087 .056

Model R
2

= .425***

* p \ .05; ** p \ .01; *** p \ .001

Table 3 Predictor variables regressed on detention decision by gender

Female Male Test of difference

e
b

(seb) e
b

(seb)

Severity of offense 1.975*** (.022) 1.553*** (.010) 10.000***

Status offense (Boot1) 0.389*** (.082) 0.286*** (.073) 2.793**

VOP (Boot2) 6.177*** (.112) 3.967*** (.056) 3.544***

Mental health need 1.005 (.035) 1.163*** (.024) -3.476***

Trauma score 1.257*** (.017) 1.063*** (.013) 8.000***

Race–Black 2.004*** (.063) 2.052*** (.040) -0.320

Ethnicity–Hispanic 1.557*** (.064) 1.822*** (.039) -2.090*

Age at target referral 0.935* (.034) 1.065*** (.018) -3.421***

Age of first referral 1.009 (.032) 0.954** (.016) 1.528

Prior offense history 1.261*** (.025) 1.171*** (.011) 2.741**

Prior probation referrals 1.159* (.065) 1.083** (.027) 0.971

Constant 0.103*** (.296) 0.088*** (.172) 0.480

Model Pseudo R
2

.243*** .203***

* p \ .05; ** p \ .01; *** p \ .001

1830 J Youth Adolescence (2013) 42:1824–1836

123

offenses. The exponentiated logistic regression coefficients

indicated for a violation of probation, a girl’s chance of

being detained almost doubled

(e
b

= 6.177) that of a boy’s

(e
b

= 3.967). Current offense severity and prior offense

history increased a girl’s odds of being detained slightly

more than a boy’s. Scoring in the warning cutoffs on the

MAYSI-2 was not found to be statistically significant in

predicting detention for girls. For boys, however, higher

mental health need increased the odds of being detained.

Elevations on the traumatic experience scale increased the

likelihood of detention more for girls than for boys.

Non-secure Placement

Analysis of the predictor variables on non-secure place-

ment, presented in the first panel of Table 4, indicates age

Table 4 Multinomial logistic regression model predicting post-adjudication placement by gender

Female Male Test of difference
e
b
(seb) e
b
(seb)

Non-secure placement

Severity of offense 1.118** (.037) 1.446*** (.028) -5.539***

VOP (Boot2) 3.167*** (.136) 7.990*** (.122) -5.003***

Mental health need 1.144** (.051) 1.097 (.055) 0.547

Trauma score 1.193*** (.030) 1.033 (.035) 3.130**

Race–Black 0.972 (.121) 0.865 (.110) 0.716

Ethnicity–Hispanic 0.806 (.127) 0.926 (.109) -0.832

Age at target referral 0.918 (.052) 0.837*** (.038) 1.444

Age of first referral 0.966 (.048) 0.995 (.034) -0.493

Prior offense history 1.243*** (.032) 1.386*** (.022) -2.809**

Prior probation referrals 1.595*** (.078) 1.103 (.069) 3.548***

Detention 19.408*** (.173) 5.665*** (.124) 5.789***

County operated secure placement

Offense composite 1.601*** (.087) 1.178*** (.015) 3.447***

VOP (Boot2) 17.108*** (.311) 2.439*** (.064) 6.135***

Warnings 0.585*** (.184) 1.092** (.033) -3.342***

Trauma score 1.286*** (.057) 1.026 (.020) 3.742***

Race–Black 1.549 (.300) 1.009 (.064) 1.397

Ethnicity–Hispanic 1.453 (.304) 1.044 (.064) 1.064

Age at referral 1.040 (.096) 0.823*** (.025) 2.354**

Age of onset 0.809* (.086) 1.193*** (.021) -4.384***

Prior offense history 1.276*** (.059) 1.253*** (.013) 0.315

Prior probated dispositions 1.554** (.133) 2.669*** (.033) -3.949***

Detention 12.988*** (.432) 5.505*** (.059) 1.986*

State correctional commitment

Offense composite 2.174*** (.108) 2.136*** (.030) 0.161

VOP (Boot2) 11.990*** (.419) 12.452*** (.124) -0.087

Warnings 0.962 (.138) 1.267*** (.046) -1.897

Trauma score 1.399*** (.074) 1.110** (.031) 2.875**

Race–Black 0.868 (.351) 1.216 (.111) -0.918

Ethnicity–Hispanic 1.056 (.346) 1.222 (.111) -0.402

Age at referral 1.120 (.126) 0.934 (.037) 1.382

Age of onset 0.746** (.105) 1.060 (.029) -3.229***

Prior offense history 1.696*** (.072) 1.664*** (.021) 0.253

Prior probation referrals 2.397*** (.134) 2.936*** (.043) -1.440

Detention 4.337*** (.393) 2.097*** (.089) 1.801

Model Pseudo R
2

.435*** .456***

* p \ .05; ** p \ .01; *** p \ .001

J Youth Adolescence (2013) 42:1824–1836 1831

123

at target referral was negatively related to confinement for

males. Greater offense severity and offense history

increased the likelihood of a non-secure placement for both

genders, although these variables were less important for

girls than boys. A violation of probation increased the odds

of non-

secure placement for boys (e
b

= 7.990) at about

2� times that of girls (eb = 3.167). The only legal variable
that increased a girl’s odds of incarceration in a non-secure

facility relative to a boy’s was prior probation history.

Neither mental health need nor trauma score was found to

be a statistically significant predictor of placement in a

non-secure facility for boys. For girls, however, elevations

on either mental health need or trauma increased the odds

of placement in a non-secure facility. Detention, an out-

come found to be strongly related to trauma and VOP for

girls, when inserted into the current model was shown to

influence non-secure confinement for both genders. How-

ever, the influence of detention was uneven in the multi-

nomial model, increasing the odds of a girl’s confinement

by 3� times that of a boy’s. With an odds multiplier of
nearly 20, detention was by far the strongest predictor of a

girl’s non-secure confinement.

County Operated Secure Placement

The second panel of Table 4 presents results from the

multinomial logistic regression analysis of the predictor

variables on secure out-of-home placement by gender.

Current offense severity and prior offense history increased

the odds of secure placement for both genders, but current

offense increased the likelihood of placement by a greater

margin for girls than boys. The analysis also revealed age

at first referral was a negative predictor of secure place-

ment for girls, yet for boys it had a positive relationship.

While a violation of probation (VOP) increased the odds of

secure placement for boys (e
b

= 2.439), girls with a VOP

experienced about 7 times greater risk of secure out-of-

home placement than boys (e
b

= 17.108). Overall mental

health need was a statistically significant predictor of

secure placement for boys; yet for girls, higher mental

health need resulted in decreased odds of secure placement.

However, elevated trauma scores increased the risk of

placement in a county-operated secure post-adjudication

facility for girls. Detention was again more strongly related

to the placement decision for girls than for boys.

State Correctional Commitment

The third panel of Table 4 presents the influence of the

predictor variables on state correctional commitment. The

models indicate that severity of the offense and prior

offense history were highly predictive of state commitment

for both genders. Severity of offense and offense history

resulted in virtually the same odds of commitment by

gender, as did a current violation of probation and prior

probation. Age at first referral was not significant for boys.

However, for girls it was negatively related, indicating

earlier starts to offending by girls were more likely to

evoke the most severe sanction available. Both mental

health need and trauma scores were significant for boys,

with only the trauma scale being significant for girls. Girls

had a higher risk of commitment based on reports of

trauma in comparison to boys. While detention was a

significant predictor of state commitment for both genders,

it appears to be twice as influential for girls. It should be

noted that although not statistically significant in a two-

tailed Z-test, the coefficients would have been significant if

a one-tailed test had been employed. Because the pattern

matches the results from the other types of confinement,

the relative size of the test coefficients suggests the dif-

ferences between genders are reliable.

Discussion

The overarching purpose of this study was to evaluate the

influence of gender and mental health need on out-of-home

placement for youth involved with the juvenile justice

system. Previous research suggested a relationship between

female delinquency and mental health need. It initially was

hypothesized that female juvenile offenders were ordered

to out-of-home placement at a higher rate for lesser

offenses than males with similar mental health needs.

Contrary to this hypothesis, the combined OLS model

regressed on the level of placement variable indicated that,

overall, being male increased a juvenile’s likelihood of

being placed in more restrictive placements. Interestingly,

mental health need was a significant, but relatively small,

predictor of placement severity, while trauma indicators,

age at first referral and violation of probation were the most

significant predictors.

Although results supported the hypothesis that mental

health need had an influence on out-of-home placement

and placement severity, it was clear a history of traumatic

experiences was a more influential factor in placement

decisions regardless of gender. However, when the analysis

included the influence of status offenses and violations of

probation, the influence of past traumatic experiences

appeared to be especially influential for girls. Other

research studies, as well as findings from this study, sug-

gest several possible explanations for this relationship.

Research has shown adolescents and adults who have

experienced childhood trauma are at an increased risk for a

variety of mental health problems, as well as many

behaviors leading to familial and legal difficulties, such as

substance abuse, promiscuity, teen pregnancy, running

1832 J Youth Adolescence (2013) 42:1824–1836

123

away, and aggression (Felitti et al. 1998). This relationship

has been shown to be dose dependent, meaning the greater

the cumulative number of traumatic experiences, the

greater the risk, which mirrors the dose response found in

the current study.

Research has shown offenders with mental health dis-

orders are much less successful under supervision in the

community than those without mental health disorders

(Monahan et al. 2005; Skeem et al. 2006; Solomon et al.

2002). This seems to be particularly true for girls, who

have been found to have higher rates of mental health

needs than their male counterparts and respond less posi-

tively to involvement in the system (Teplin et al. 2002;

Wasserman et al. 2005). The findings of this study extend

this previous research, suggesting that through the use of

violation of probation, girls and youth of both genders with

documented mental health needs are funneled deeper into

both county and state operated out-of-home placements

than boys and those without a mental health needs. These

effects also appear to be cumulative, with pre-adjudicatory

decisions influencing post-adjudicatory outcomes. Factors

influencing detention decision for girls, expressly traumatic

experiences and bootstrapping, continue to indirectly

influence post-adjudicatory decisions.

The trend identified for trauma experiences to increase

the risk for youth to be placed in more restrictive settings

may be counter-productive. Some researchers postulate

traumatic stress symptoms may be worsened as a result of

being involved in the juvenile justice system. Youth being

placed out of the home, especially in more secure settings,

may be further traumatized by separation from familiar

adults, exposure to aggressive or threatening peers, and by

feelings of threat and lack of safety. The characteristics of

the environment, including traditional confrontational

methods of maintaining order, may backfire for girls with

traumatic stress symptoms (Griffin 2002; Hennessey et al.

2004). Seclusion and restraints have been cited as an

example of a practice in institutions that can be especially

re-traumatizing (Huckshorn 2006). Prescott (1997) indi-

cated the cycle of staff interventions, especially during

times of crisis, led to increased self-injury in response to

the use of physical and mechanical restraints. Due to their

high rates of traumatic stress and the possibility of re-

traumatization through incarceration, girls may be espe-

cially susceptible to worsened traumatic stress sympto-

mology (Hennessey et al. 2004).

Although this study is an important step in better

understanding the influence of gender and mental health

need on juvenile justice placement decisions, several lim-

itations to the methodology should be noted. One important

limitation to note is the study was limited to three urban

juvenile probation departments in one state. Additional

studies should be undertaken to replicate the findings in

other states and jurisdictions to determine its generaliz-

ability. Similarly, differences between jurisdictions were

not identified in the analyses conducted and should be

explored in future research. In addition, the study was

limited to the variables available within the county

administrative systems. This did not allow for the inclusion

of other measures of mental health need or trauma that may

have been more sensitive than a screening measure. It also

did not allow for the inclusion of extralegal variables that

may influence a court’s decision to put a juvenile in out-of-

home placement, such as home environment, community-

based resources, or other process-oriented measures.

Although the study design had some limitations, there

are also many strengths that result in this study contributing

to the literature. One strength of the study is the relatively

large sample size and the longitudinal nature of the data

collected. The data set analyzed included demographic as

well as lifetime arrest, disposition and mental health data

collected on over 30,000 individual youth who were

referred to the participating juvenile probation departments

during the 2 year sample period. Combined, the partici-

pating juvenile probation departments account for over

51 % of the state’s total juvenile population each year.

Another strength of this study was the use of multiple

probation departments. All three departments resided in

urban settings, but differed in geographic location and

ethnic composition. One department was located in north

Texas with a primary minority population of African

American youth. Another was located in south Texas with

a primary minority population of Hispanic youth. The final

department was located in central Texas with comparable

representation of African American and Hispanic youth. In

addition, well-delineated variable definitions and the

inclusion of many relevant predictor variables further

strengthened the study. This was evidenced by the rela-

tively large amount of variance predicted in the tested

models.

Conclusion

The above findings suggest the importance of trauma

informed care, both to better address the impact of trauma

on criminogenic risk and improve the success rate of youth

with mental health needs on supervision. One important step

in the last decade is a focus on trauma-informed systems,

including juvenile justice systems. The National Child

Traumatic Stress Network defines a trauma-informed child

and adolescent service system as one in which all programs

and agencies ‘‘infuse and sustain trauma awareness,

knowledge, and skills into their organizational cultures,

practices, and policies’’ (National Child Traumatic Stress

Network 2012). In a trauma informed juvenile justice

J Youth Adolescence (2013) 42:1824–1836 1833

123

system, judges and probation officers are knowledgeable

about the impact of trauma and respond to youth with this

context in mind. All youth are screened routinely for

exposure to trauma and evidence-based trauma treatments

are available. Behaviors that may be attempts to cope with

current or past traumatic experiences, such as substance

abuse, interpersonal aggression, risky sexual behavior, self-

injury, gang affiliation, and running away, are recognized as

a symptom of mental health difficulties and addressed with a

trauma lens, rather than strictly a legal one.

Although, this study replicated the findings of several

other prevalence studies that youth in out-of-home juvenile

justice placements have higher rates of mental health need

than those remaining in the community, it also highlighted

the need for juvenile justice systems to enhance their

awareness of the role of trauma in juvenile delinquency and

transform the system to address and support youth who

have experienced significant trauma in their childhood.

Additional research is needed to determine the effective-

ness of trauma interventions for youth in the various

components of the juvenile justice system (e.g., prevention

programs, community-based supervision, detention, non-

secure and secure facilities, parole). For some youth, their

trauma history coupled with their involvement in the

juvenile justice system can lead to the development of post-

traumatic stress disorder (PTSD), depressive disorders, and

anxiety disorders. Without effective treatment and/or

involvement in a trauma informed system, these disorders

are likely to continue to cause impairment and may result

in negative long-term outcomes. The reality is juvenile

justice systems have public safety, and not mental health

treatment, as their primary goal and significant organiza-

tional change will be needed to embrace the goal of

becoming a trauma-informed system.

Acknowledgments The authors would like to acknowledge the
local juvenile probation departments for their support and collabo-

ration during the data collection and study design efforts conducted

for this study.

Author contributions Each author contributed to the development
of the study and the resulting article submission. EE was the primary

researcher and conceived the study and performed the initial analysis

and the initial implications of the study. JJ assisted with the initial

analysis and conducted additional analysis to examine the exploratory

hypothesis. ML contributed to the interpretation of the analysis and

the impacts of trauma and trauma informed care. All three authors

helped to write the article and approved the manuscript’s content.

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Services, 53(1), 50–56.

Steiner, H., Garcia, I., & Mathews, Z. (1997). Posttraumatic stress

disorder in incarcerated juvenile delinquents. Journal of Amer-

ican Academy of Child and Adolescent Psychiatry, 36(3),

357–365.

Teplin, L., Abram, K., McClelland, G., Dulcan, M., & Mericle, A.

(2002). Psychiatric disorders in youth in juvenile detention.

Archives of General Psychiatry, 59(12), 1133–1143.

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202012 .

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juvenile justice system in Texas for calendar year 2008. (TJPC

Publication No. RPT-STAT-2008). Retrieved from www.tjpc.

state.tx.us/publications/reports/RPTSTAT2008 .

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correlates studies: Findings and policy implications. Juvenile

Justice, 9(1), 3–19.

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(2005). Gender differences in psychiatric disorders at Juvenile

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131–137.

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III criteria for conduct disorder in preadolescent girls. Journal of

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461–470. doi:10.1097/00004583-199604000-00012.

Author Biographies

Erin M. Espinosa is a research associate in the Texas Institute for
Excellence in Mental Health at the Center for Social Work Research

at the University of Texas in Austin. She received her doctorate in

juvenile justice from Prairie View A&M University. Her primary

areas of interest and research are mental health and juvenile justice,

trauma based recovery and special populations, juvenile competency,

developmental perspectives for juvenile justice, and implementation

of Evidence Based Practices (EBPs) in criminal justice.

Jon R. Sorensen is a Professor of Criminal Justice at East Carolina
University. He received his doctorate in criminal justice from Sam

Houston State University. His major research interests include prison

violence, capital punishment, and racial disparity in the justice

system. He has published articles on prison violence, capital

J Youth Adolescence (2013) 42:1824–1836 1835

123

http://dx.doi.org/10.1177/1077801206288142

http://www.hogg.utexas.edu/uploads/documents/Trauma_Inform_Care_091410

http://www.hogg.utexas.edu/uploads/documents/Trauma_Inform_Care_091410

http://dx.doi.org/10.1007/s10488-005-0011-5

http://dx.doi.org/10.1007/s10488-005-0011-5

http://dx.doi.org/10.1177/0011128701047002002

http://www.nctsn.org/resources/topics/creating-trauma-informed-systems

http://www.nctsn.org/resources/topics/creating-trauma-informed-systems

http://dx.doi.org/10.1023/A:1025054825467

http://dx.doi.org/10.1177/0002716205280075

http://dx.doi.org/10.1177/0002716205280075

http://www.aecf.org/publications/data/jdai_pathways_girls

http://www.aecf.org/publications/data/jdai_pathways_girls

http://www.ojjdp.ncjrs.org/ojstatbb/cjrp/

http://www.ojjdp.ncjrs.org/ojstatbb/cjrp/

http://dx.doi.org/10.1177/0093854805284420

http://dx.doi.org/10.1177/0093854805284420

http://www.tjjd.texas.gov/publications/reports/TJJD%20Strategic%20Plan%20-%20FINAL%20-%20JULY%202012

http://www.tjjd.texas.gov/publications/reports/TJJD%20Strategic%20Plan%20-%20FINAL%20-%20JULY%202012

http://www.tjjd.texas.gov/publications/reports/TJJD%20Strategic%20Plan%20-%20FINAL%20-%20JULY%202012

http://www.tjpc.state.tx.us/publications/reports/RPTSTAT2008

http://www.tjpc.state.tx.us/publications/reports/RPTSTAT2008

http://dx.doi.org/10.1097/00004583-199604000-00012

punishment, and racial disparity in the criminal justice system. He is

coauthor of Lethal Injection: Capital Punishment in Texas During the

Modern Era (2006, University of Texas Press).

Molly A Lopez is the Director of the Texas Institute for Excellence
in Mental Health, a licensed clinical psychologist and a Research

Associate Professor at the University of Texas at Austin, School of

Social Work. She received her doctorate in clinical psychology

from Texas A&M University. Her research interests include mental

health services, child and adolescent service systems, the imple-

mentation of evidence-based practices, and cognitive behavioral

therapies.

1836 J Youth Adolescence (2013) 42:1824–1836

123

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Article

Treatment Services in
the Juvenile Justice System:
Examining the Use and
Funding of Services by
Youth on Probation

Clair White
1

Abstract
Youth enter the juvenile justice system with a variety of service needs, particularly for mental
health problems. Research has examined the extent to which youth have mental health disorders,
primarily among detained youth, and factors associated with treatment referrals, but little
research has examined youth on probation and the actual use of services. Using data obtained
from the Maricopa County Juvenile Probation Department from July 2012 through August 2014
(N ¼ 3,779), the current study examines (1) the factors associated with receiving treatment
services while on probation and (2) the factors associated with receiving treatment services
through different funding streams. Findings reveal that only about 25% of the sample of youth on
probation received treatment services, suggesting the underservicing of youth. Consistent with
prior research, there were also racial and ethnic disparities concerning treatment use, with Blacks
and Latinos less likely to receive services. Additionally, certain characteristics of youth and their
background influenced the funding source for treatment services. Implications for policy and
research are discussed in light of these findings.

Keywords
probation, treatment services, service use, juvenile justice, racial/ethnic disparities

The juvenile justice system has multiple responsibilities often serving conflicting goals of punitive

sanctions and rehabilitative treatment (Bishop, 2006; Lipsey, Howell, Kelly, Chapman, & Carver,

2010). The system must not only address the current delinquent behavior but also, in many cases,

consider the health and well-being of the youth. Youth come into the juvenile justice system with

more complex problems and greater needs for mental and behavioral health services, which has

resulted in more attention on efforts to rehabilitate and address youth’s mental and behavioral

1
Center for Evidence-Based Crime Policy, Criminology, Law and Society, George Mason University, Fairfax, VA, USA

Corresponding Author:

Clair White, Center for Evidence-Based Crime Policy, Criminology, Law and Society, George Mason University, 4400

University Dr., MS 6D12, Fairfax, VA 22030, USA.

Email: cwhite28@gmu.edu

Youth Violence and Juvenile Justice
2019, Vol. 17(1) 62-87
ª The Author(s) 2017
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service needs (Myers & Farrell, 2008). Research has examined a number of issues related to mental

health and behavioral health problems of youth in the juvenile justice system, particularly identify-

ing the rates of mental health problems and service needs among youth and factors associated with

treatment referrals of youth in different systems of care (i.e., juvenile justice system and mental

health system).

Research on mental health problems in justice-involved youth has primarily focused on the

service needs of youth and where they have been referred to meet these needs and not on whether

they actually received those services. Additionally, much of the work examines youth in detention or

compares youth sentenced to community versus correctional supervision rather than youth on

probation which is the predominate sentence in the juvenile justice system. The current study uses

juvenile probation data from a large, urban jurisdiction in Arizona to examine these issues. More

specifically, legal and extralegal factors associated with the use of treatment services among youth

on probation supervision are examined. Furthermore, the extent to which services are funded by the

juvenile justice system has not been empirically examined, therefore, whether these services are

funded by the juvenile justice system or external funding sources such as Medicaid or private

insurance is also examined.

Unmet Service Needs and Treatment Referrals

Youth involved in the juvenile justice system often experience multiple adversities or risk factors,

such as economic disadvantage, experiences of abuse and neglect, unstable family environments,

exposure to drugs and alcohol, and mental illness (Esbensen, Peterson, & Taylor, 2010; Huizinga,

Loeber, Thornberry, & Cothern, 2000; Loeber & Farrington, 1998). Research has generally found

that 65–70% of youth in juvenile justice facilities, primarily detention centers and correctional
facilities, suffer from at least one mental health disorder (Shufelt & Cocozza, 2006; Teplin, Abram,

McClelland, Dulcan, & Mericle, 2002; Wasserman, McReynolds, Lucas, Fisher, & Santos, 2002),

while rates among youth on probation are approximately 50% (Wasserman, McReynolds, Ko, Katz,
& Carpenter, 2005).

Additionally, comorbidity, or the presence of more than one mental or behavioral disorder, is

particularly high among youth in juvenile justice settings (Abram, Teplin, McClelland, & Dulcan,

2003; Kessler et al., 1996; Teplin et al., 2002). Shufelt and Cocozza (2006) found that roughly 79

%

of those who met criteria for at least one mental health disorder had two or more diagnoses.

Unfortunately, many of these mental and behavioral service needs are not met in the community

(Flisher et al., 1997; Jensen et al., 2011; Kataoka, Zhang, & Wells, 2002; Ringel & Sturm, 2001). As

a result, the coexistence of multiple disorders in addition to other criminogenic risk factors makes

prioritizing mental and behavioral service needs more challenging for the juvenile justice system

(Grisso, 2004).

Research has examined factors related to unmet service needs and the avenues through which

youths’ mental health needs are met through various service sectors, such as the mental health

system and juvenile justice system (Burns et al., 2004; Stahmer et al., 2005; Thompson, 2005).

Among the general population, children and adolescents with mental and behavioral health problems

are gravely undertreated with high rates of unmet service needs (Angold et al., 1998; Flisher et al.,

1997; Horwitz, Gary, Briggs-Gowan, & Carter, 2003). Studies have examined characteristics of

children with unmet mental health needs and their families using various samples to identify key

predictors of treatment service use and unmet service needs.

Among the primary factors associated with unmet service needs are elements related to economic

disadvantage such as living on public assistance, lack of health insurance, and transportation prob-

lems (Chow, Jaffee, & Snowden, 2003; Cornelius, Pringle, Jernigan, Kirisci, & Clark, 2001; Haines,

McMunn, Nazroo, & Kelly, 2002). Race and ethnicity are also strong predictors of unmet service

White 63

needs with Whites being more likely to receive mental health services compared to minorities

(Angold et al., 2002; Garland et al., 2005; Kataoka et al., 2002; Thompson, 2005; Yeh, McCabe,

Hough, Dupuis, & Hazen, 2003). Studies have also found that minorities have limited opportunities

to access mental health services (Arcia, Keyes, Gallagher, & Herrick, 1993), and once they start

treatment they are less likely to complete treatment (Kazdin, Stolar, & Marciano, 1995).

Research has also found involvement in the mental health system increases the likelihood of

being referred to the juvenile justice system (Cohen et al., 1990; Evens & Stoep, 1997; Rosenblatt,

Rosenblatt, & Biggs, 2000). In addition, younger adolescents, females, and White youths are more

likely to be referred to the mental health system, while minorities, males, and youths with more

serious and disruptive mental health disorders are more likely to be referred to the juvenile justice

system (Atkins et al., 1999; Cohen et al., 1990; Dembo, Turner, Borden, & Schmeidler, 1994; Evens

& Stoep, 1997). In general, service needs of disadvantaged and minority youth are often not

recognized until their contact with the juvenile justice system (Golzari, Hunt, & Anoshiravani,

2006; Rawal, Romansky, Jenuwine, & Lyons, 2004; Rogers, Pumariega, Atkins, & Cuffe, 2006).

Upon entering the juvenile justice system, service needs often continue to go unmet even after

identification of need for treatment (Rogers, Zima, Powell, & Pumariega, 2001; Shelton, 2005).

Shelton (2005) found that only 23% of youth diagnosed with mental health disorders received
treatment and that having a mental disorder was not a significant predictor of receiving services.

A recent study conducted by Hoeve, McReynolds, and Wasserman (2014) found that youth with

externalizing disorders and substance use disorders were more likely to receive referrals, while only

40% of youth with internalizing disorders referred to service. Consistent with the findings from the
general public, Whites are more likely to be referred to services compared to Black youth in the

justice system (Dalton, Evans, Cruise, Feinstein, & Kendrick, 2009; Lopez-Williams, Stoep, Kuro,

& Stewart, 2006; Maschi, Hatcher, Schwalbe, & Rosato, 2008; Rogers et al., 2006), but there are

some mixed findings (Breda, 2003; Hoeve et al., 2014). Shelton (2005) concluded that

while the total responsibility for the well-being of children does not lie solely with the juvenile justice

system, the decision not to provide treatment services to youth in need and under their care implies

neglect . . . it implies a perception that these youth will go away, be treated elsewhere, or grow out of their

problems. (p. 110)

These prior studies do not provide a clear set of predictors for service referrals and many studies

were not able to control for offense severity and criminal history (Dalton et al., 2009; Lopez-

Williams et al., 2006; Rawal et al., 2004), which are likely to influence referrals for services.

Regardless, there were discrepancies in service referrals in the juvenile justice system. Receipt of

service referrals was not found to be dependent entirely on the need for services but may be

influenced by other factors that create disparities in the health of youth. Furthermore, these studies

did not take into account access (i.e., availability, health insurance, etc.) to referred services or

whether youth were actually using the services.

Many of the studies previously discussed use referrals for treatment services as the outcome of

interest, but little research has examined the actual receipt or use of treatment services by youth

(Teplin, Abram, McClelland, Washburn, & Pikus, 2005). Teplin, Abram, McClelland, Washburn,

and Pikus (2005) found that roughly 16% of youth who had been identified as needing mental health
services during detention received services within 6 months from detention or by disposition.

Additionally, 11% of youths received services but did not meet the definition of need. Johnson
et al. (2004) examined substance abuse treatment need and use among youth entering juvenile

corrections and found that nearly half of youth with need for substance abuse treatment received

services. Rawal, Romansky, Jenuwine, and Lyons (2004) examined racial differences in mental

health needs and service use among incarcerated youth. The authors found that Blacks had the

64 Youth Violence and Juvenile Justice 17(1)

greatest level of mental health needs, but the lowest level of prior and current service use. In general,

these studies emphasize how few individuals actually receive services for their mental and beha-

vioral service needs as well as the “benign neglect” of the juvenile justice system in addressing

mental and behavioral service needs (Herz, 2001).

Lastly, receiving referrals for treatment or participating in certain programs and treatment does

not necessarily translate into needs being met (Grisso, 2004). The justice system has the difficult task

of distinguishing youths’ need for specific programs that target criminogenic risk factors from the

need for treatment services that address their overall mental well-being. Given limited training and

resources, some needs are often prioritized over others, leaving other needs unaddressed (Haqanee,

Peterson-Badali, & Skilling, 2015). Responsivity is a key component of the risk-needs-responsivity

(RNR) model in offender treatment, emphasizing matching program and treatment plans to meet the

unique reoffending risks and risk factors (i.e., criminogenic needs) of offenders through evidence-

based rehabilitative programs that are tailored to an individual’s strengths and capacities (Andrews

& Bonta, 2010; Andrews, Bonta, & Hoge, 1990; Hoge & Andrews, 1996). Rather than general

mental health (GMH) care, the RNR model is focused on reducing future delinquency and recidi-

vism but has been criticized for not addressing more basic, noncriminogenic, human needs, such as

mental health (T. Ward & Stewart, 2003; T. Ward, Yates, & Willis, 2012). Additionally, treating

mental health and substance abuse disorders may or may not address other criminogenic risk factors

and prevent future delinquency (see Wibbelink, Hoeve, Stams, & Oort, 2017) but may have impli-

cations for youths’ responsiveness to treatment goals and success in addressing criminogenic needs

(Haqanee et al., 2015). Nevertheless, programs that adhere to the principles of RNR have been

successful in reducing recidivism (Andrews & Bonta, 2010).

One of the primary RNR assessment tools, the Youth Level of Service/Case Management Inven-

tory (YLS/CMI), has been validated for its ability to predict recidivism among youth (Catchpole &

Gretton, 2003; Jung & Rawana, 1999; Onifade et al., 2008; Vieira, Skilling, & Peterson-Badali,

2009). However, agencies and practitioners face many challenges to develop clear treatment plans

and effectively implement services despite identifying risks and needs through assessment (Flores,

Travis, & Latessa, 2004; Latessa, Cullen, & Gendreau, 2002; Sutherland, 2009), resulting in many

youths’ needs left unaddressed (Vieira et al., 2009). This “implementation gap” is often the result in

the availability of quality, evidence-based programming, such as cognitive behavioral therapy

(Haqanee et al., 2015). For example, Flores, Travis, and Latessa (2004) found in one state jurisdic-

tion that the RNR tool (YLS/CMI) was widely used, but when it came to services in the treatment

plans, they rarely targeted the needs identified in the assessment. In sum, there have been great

strides in recognizing and measuring criminogenic risks and needs that when addressed can improve

outcomes for youth. Mental illness, however, is often not considered one of those criminogenic

needs (Haqanee et al., 2015), so practitioners may continue to use their clinical judgment and

experience over the use of risk assessment tools (C. Schwalbe, 2004), and services received may

not target the needs/risks identified.

Funding Treatment Services

While the juvenile justice system has a legal mandate to provide treatment services, it does not have

to be the one to administer that care (Grisso, 2004). When a youth is required to receive court-

ordered treatment services as a condition of probation supervision, there are multiple avenues or

sources of funding that can pay for these services. If the youth has no means (i.e., health insurance)

to pay for treatment services ordered by the court, the juvenile justice system has a financial

responsibility to fund the treatment services it is requiring.

The juvenile justice system has used outside agencies and external funds to reduce the burden of

providing treatment services—they typically contract out to private providers or other government

White 65

agencies such as public mental health service providers. Similarly, the treatment services can be

funded through different sources such as private insurance or public health care, but if those avenues

are not available, the juvenile justice system is responsible to fund the treatment services. Families

of youth in the juvenile justice system often have limited knowledge and resources to navigate the

health-care system; therefore, youth often are more likely to be uninsured and their mental and

behavioral conditions are not addressed. Furthermore, services provided through Medicaid are often

restricted to children with the most severe mental disorders due to lack of funding (Kerker & Dore,

2006). As a result, children with less serious problems are often ineligible for services and those who

do qualify receive inconsistent and fragmented care. Finally, studies have found that lack of health

insurance is a major impediment to obtaining mental and behavioral health services (Farmer, Stangl,

Burns, Costello, & Angold, 1999; Flisher et al., 1997; Kataoka et al., 2002).

In light of the health-care debate, the current research also speaks to the issue of funding and

resources for mental health care and substance use disorder services that are often subject to social,

political, and economic influence. The coverage for mental health and substance use disorders by

insurance companies and the availability and eligibility of Medicaid will likely have implications for

practices in the juvenile justice system and the extent to which treatment services are court-funded.

If youth have alternative sources to pay for treatment services, such as private insurance or Med-

icaid, the juvenile justice system will be relieved of that responsibility. While the current research

does not empirically evaluate health-care reform on funding treatment services in the juvenile justice

system, findings should be considered in the context of these broader changes.

The funding of treatment services in the juvenile justice system has not been examined as a key

variable of interest. While the source of funding for treatment services is often determined by the

youth’s health care coverage, the court also considers the need for services, prioritizing those with

greatest need. However, as was demonstrated with literature on unmet service needs, need does

not necessarily result in the expected outcomes (i.e., services). Following this line of thought,

there may be other factors that could influence the court’s decision to fund treatment services.

Furthermore, the quality of services and degree of investment the court has when it is funding the

treatment services may differ, which may have implications for the future delinquent behavior and

overall health of the youth.

Current Focus

Building on previous research on service needs and use among youth with mental and behavioral

problems, this research examined treatment services received by youth involved in the Maricopa

County Juvenile Probation Department (MCJPD). The court serves youth by requiring treatment

services for mental and behavioral problems but providing resources to pay for treatment services

adds an additional level of intervention and investment in these youth’s lives. The current research

examined characteristics of youth who received treatment services as well as funding sources for

services. More specifically, two research questions are examined:

Research Question 1: What are the predictors (e.g., gender, race, delinquent background,

etc.) associated with receiving treatment services under probation supervision?

Research Question 2: Among youth receiving treatment services, what are the predictors

associated with the source of funding for treatment services; specifically, what are the pre-

dictors of receiving treatment services via external funding sources relative to court-based

funding?

Research on mental and behavioral service needs and service referrals has generally focused on

treatment for mental health and substance use disorders, but youth can have other service needs. The

66 Youth Violence and Juvenile Justice 17(1)

current research is not restricted to mental health and substance use treatment services and is

more inclusive of other treatment services provided by the juvenile justice system, such as

behavior-specific education, mentoring programs, and evidence-based programs. Based on previous

research, we expect race/ethnicity to be a strong predictor of service use as well as prior history of

mental health problems and involvement in the juvenile justice system.

This research will also shed light on which types of services are typically funded by the court. The

ever-changing financial climate and the health-care debate provide a broader context that can help

inform the importance of understanding the sources of funding for treatment services. There is

growing concern for addressing service needs, particularly for mental health and substance use

disorders, but with limited resources, the funding sources of treatment services deserves empirical

attention. Given the limited attention on the issue of funding, this question is more exploratory in

nature. The implications of this research will help to inform broader issues of the juvenile justice

system’s obligation to provide treatment.

Method

Data and Sample

The MCJPD and the Treatment Services Division were sources for data regarding youth receiving

treatment services. The time frame for the data spanned a 25-month period beginning July 1, 2012, to

August 31, 2014, during which a total of 4,244 youth were placed on probation, 60 of whom had

multiple probations during the time frame.
1

The data were compiled onsite with the assistance from

the Research and Planning Division of the MCJPD. A data sharing agreement was obtained with

institutional review board approval to receive deidentified youth information through electronic

databases. With the exception of certain files, such as psychological case notes,
2

MCJPD uses the

integrated court information system to manage youths’ records, and Microsoft
®

Access was used to

query databases associated with youth who were placed under probation supervision during the

specified time frame.
3

For purposes of this analysis, the unit of analysis was the individual youth. Eight different databases

were used to measure the legal and extralegal characteristics of the youth and their case. The databases

were cleaned as separate files and merged based on each youth’s unique identifier. The data required

recoding variables and MCJPD advised to ensure the recoded variables accurately measured the

correct information. For example, the complaint data set contained all referrals (or complaints) the

youth has received in Maricopa County. The unit of analysis in this data set was referrals, and there

were 17,784 referrals for the 4,244 youth analyzed in the current research. This data set, in particular,

took an extensive amount of cleaning and management because it was used to (1) identify which

referral was associated with the disposition that placed the youth on probation and the severity of that

offense, and (2) determine the number of referrals and adjudications that occurred before the current

probation to measure prior offending behavior.

The final sample of youths on probation was 3,779 after those with short probation periods (less

than 10 days) and cases with missing data were removed.
4

Descriptive statistics of the sample of

youth are presented in Table 1. Similar to other research on juvenile justice populations, a majority

of the sample was male (81.2%), roughly 37% of the sample were White, 15% Black, and 41%
Hispanic, and the mean age was 16.1 years old. A majority of the youth came from single parent

living situations (60.8%) and a quarter were not enrolled in school. In regard to the youths’ offense
and juvenile justice history, property felonies were the most common (25.1%), followed by personal
felonies (19.1%), 40.5% were detained prior to adjudication, 67.1% had a prior referral, and 12.9%
had a prior adjudication. Additionally, 37.5% of youth received a psychological evaluation

White 67

associated with the current offense, 18.3% had prior treatment services, and in regard to risk level,
20.4% were low, 24.6% were moderate, and 55% were high risk.

The current research focused on youth who received treatment services in the community and

residential facilities while on probation, thus services received while on diversion will not be

examined, but will be captured as prior services. In 2012, MCJPD began the Service Authorization

Table 1. Descriptive Statistics of Dependent and Independent Variables.

Variables

Youth on Probation (N ¼ 3,779)

% n

Outcome variables
Receiving treatment services 25.0 944
Funding source (n ¼ 861)

Court-based 72.2 622
External 27.8 239

Independent variables
Gender

Female (reference) 18.8 712
Male 81.2 3,067

Race/ethnicity
White (reference) 37.4 1,414
Black 15.3 580
Latino 41.4 1,564
Native American 4.3 161
Other 1.6 60

Age (mean) 16.1 (1.3) 3,779
Living situation

Single parent (reference) 60.8 2,299
Two parents 19.6 741
Grandparents or other relatives 8.3 313
DCS and other 11.3 198

School status
Enrolled (reference) 75.1 2,839
Not enrolled 24.9 940

Offense severity
Property felony (reference) 25.1 948
Personal felony 19.1 720
Property misdemeanor 12.6 477
Personal misdemeanor 8.0 304
Drugs 18.8 710
Public peace 14.8 559
Other 1.6 61

Preadjudication detention 40.5 1,530
Prior referral 67.1 2,537
Prior adjudication 12.9 486
Psychological evaluation 37.5 1,416
Prior treatment services 18.3 692
Risk level

Low (reference) 20.4 770
Moderate 24.6 929
High 55.0 2,080

Note. DCS ¼ Department of Child Services.

68 Youth Violence and Juvenile Justice 17(1)

Form Automation Project to electronically track the treatment services ordered by the court and

progress of youth receiving services as part of their probation. Based on the recommendation by

Research and Planning Division, services that started 90 days prior to the start of probation will also

be considered prior services. Treatment services evaluated in the current study include GMH

services, sex offender services, substance abuse services, mentoring or life skills programs,

behavior-specific education, evidence-based programs, and drug court services.
5

Treatment services

that are not included in the current research include mandatory drug testing, detention alternative

programs, physical health services such as acute care or hospitalization, polygraph examinations,

and assessments. These services were not included because they are not therapeutic in nature and

generally not used to address mental and behavioral service needs. Among the 3,779 youth on

probation included in the analysis, 944 (25%) received the services of interest.

Measures

There are a number of legal and extralegal factors that have been examined in relation to various

outcomes in the juvenile justice system and whether youth end up in mental health system versus

juvenile justice system (Cohen et al., 1990; Evens & Stoep, 1997; Lyons, Baerger, Quigley, Erlich,

& Griffin, 2001; Thomas & Stubbe, 1996). The current study focused on the referral that placed the

youth on the current probation and treatment services, but characteristics of prior behavior are

captured. The independent variables that were used in the analyses include gender, race, ethnicity,

age, living situation, school status, offense severity, preadjudication detention, prior referrals, prior

adjudications, whether the youth received a psychological evaluation, prior treatment service use,

and risk assessment level.

Gender was coded as 1 for males and 0 for females, and race and ethnicity are measured by

several dummy variables: Blacks, Latino/Latina, and other race/ethnicity, with White as the refer-

ence category. Age is measured as the age of the youth at the time of the referral that received a

disposition of treatment services and is measured continuously. The living situation of the youth

captured who the youth lived with when they were placed on probation. The categories included

single parent, two parents, grandparents or other relative, and Department of Child Safety or other,

with single parent serving as the reference category. School status was measured on the basis of

whether or not the youth was enrolled in school during the time of the current referral. Offense

severity captured the most severe offense associated with the referral. Consistent with sentencing

research on juveniles, if the youth was charged with multiple offenses, the most serious offense was

measured. There are seven categories of offense severity—property felony, personal felony, prop-

erty misdemeanor, personal misdemeanor, drugs, public peace, and other offenses that included

obstructions of justice and status offenses. Property felony serves as the reference category because

it had the highest frequency. Preadjudication detention captured whether the youth was detained

prior to adjudication for the current offense and probation. Prior referrals and prior adjudications

are measured dichotomously, with “yes/no” outcomes. Prior service use was also a binary variable,

measuring whether the youth has received treatment services through the court from either diversion

or prior probations.

Every youth who reaches adjudication and disposition is considered for a psychological evalua-

tion, but these are predominately conducted only when there is a history of mental illness and service

need, and the court would benefit from clinical assistance. Therefore, having a psychological

assessment is a strong proxy for history of mental health problems in the current study. In addition

to the psychological evaluation, every youth completes the Arizona risk/needs assessment (ARNA)

and receives a risk level—low, moderate, or high. ARNA is an empirically validated instrument

predominately used to predict risk of future offending, but it also helps in identifying needs of youth

(see Krysik & LeCroy, 2002; C. S. Schwalbe, 2009). Following the youth’s initial intake assessment

White 69

that includes an interview with the youth and a review of records, the risk assessment items are

completed by two probation officers. The risk scale consists of a number of dimensions such as

alcohol and drug use, family relationship, assaultive behavior, extensive absenteeism or truancy at

school, peer delinquency, and emotional/behavioral problems.

The type of treatment service was also included as an independent variable for examining the

second dependent variable (source of funding) to control for services that are typically provided and

therefore funded by the court. As previously mentioned, the services youth could receive were:

GMH—residential and outpatient, sex offender—residential and outpatient, and substance abuse—

residential or outpatient, as well as mentoring and life skills, behavior-specific education, evidence-

based programs, and drug court services (see Appendix A). The most common type of treatment

service used was GMH outpatient services (29.9%) followed by residential GMH services (19.9%).
Mentoring services, behavior-specific education, evidence-based programs, and drug court services

were combined into one category because of their low frequency and they were predominately

funded and administered through MCJPD. Additionally, 247 youth received multiple services, so

these were divided into youth who received two services and youth who received three or more

services. The reference category for type of service was youth who exclusively received GMH

outpatient services.

Dependent Variables

There are two primary dependent variables that were examined in the current analysis: (1) whether

the youth received treatment services and (2) the type of funding source for treatment services. First,

to examine predictors of receiving treatment services, the dependent variable was a dichotomous

outcome of whether the youth received court-ordered treatment services (coded as 1) or not (coded

as 0). Much of the prior research examines referrals for treatment services, which can often act as a

proxy for receiving services, but since this study can identify referrals that result in the use of

treatment service, referrals for that were denied were coded as zero.

Second, to examine the next research question pertaining to the funding source for treatment

sources, the source of funding was coded as a dichotomous outcome. Given limited resources,

every youth is screened for behavioral health coverage through the state Medicaid fund, Ari-

zona Health Care Cost Containment System (AHCCCS) or Regional Behavioral Health Author-

ities (RBHA), and/or private insurance (Superior Court of Maricopa County, Juvenile Probation

Department, 2015). If the youth does not receive benefits from the private or public insurance,

the youth’s treatment services are funded by the court through the Juvenile Probation Services

Fund. Only seven youth in the sample received treatment services through private insurance, so

this category was not large enough to analyze separately. Additionally, 16 youth received

treatment services through tribal health coverage, 90 through RBHA, and 86 through AHCCCS.

These funding sources were combined into one category of external funding source (coded as

1), which is compared to court-based funding as the reference category (coded as 0) for the

multivariate analysis.
6

Analytic Strategy

The analysis will proceed in two stages. First, bivariate statistics will be estimated to identify

differences between groups using independent sample t tests and w2 to test for significance. The
second stage of the analysis involves multivariate regression models. A logistic regression model

was used to examine whether the youth received treatment services, and a two-stage full information

maximum likelihood (FIML) probit model was estimated to control for selection bias when

70 Youth Violence and Juvenile Justice 17(1)

examining the funding source dependent variable.
7

Significant variables are reported in odds ratio

(OR) for easier interpretation.

Results

Use of Services

Beginning with the first question of interest, examining factors associated with the receipt of

treatment services, the bivariate statistics describing the relationship between the independent vari-

ables and whether the youth received treatment services are presented in Table 2. As indicated by

Table 2, there was not a significant relationship between gender and receiving treatment services,

but there was a significant difference for race/ethnicity, with Latino youth were slightly under-

represented in treatment services compared to the other groups and Native Americans more repre-

sented in treatment services.

While the mean age of the youth also was statistically different across treatment service use, with

youth who received treatment services being slightly younger, the difference has little substantive or

practical meaning. There were also significant differences between youths’ living situation, and

offense severity, preadjudication detention, prior adjudication, psychological evaluation, prior treat-

ment service use, and risk level. Youth who lived with parents, particularly two parents, were least

likely to receive treatment services. Importantly, 46.5% of youth who received a psychological
evaluation received treatment services, and 12.1% of youth who did not receive a psychological
evaluation received services. This finding suggests a disconnect between need and use; while youth

with a psychological evaluation were more likely to get treatment services, there were many youth

who did not receive services. On the other end, a small number of youth received services without

having a psychological evaluation.

The results from a multivariate logistic regression are presented in Table 3. The significant

demographics included age, being Black or Latino, living with grandparents or relatives, and living

with the state (Department of Child Services [DCS]) or other living arrangements. More specifically,

the effect of age is negative, meaning that as age increases the likelihood of receiving treatment

services decreases. In regard to race and ethnicity, Blacks and Latinos are less likely to receive

treatment services than their White counterparts, 33.4% (OR ¼ .666) and 21.9% (OR ¼ .781),
respectively. In terms of the youth’s living situation, youth who live with grandparents or relatives

or DCS were more likely to receive treatment services than youth living with single parents.

Specifically, youth under DCS care were more than 2 times (OR ¼ 2.032) more likely to receive
treatment services. There was no significant difference between youth who lived with two parents

versus youth who lived with a single parent on the likelihood of receiving services.

Other significant variables included preadjudication detention, prior adjudication psychological

evaluation, and risk level. Both the effect of being detained and having a prior adjudication reduced

the likelihood of receiving treatment services, 23% (OR ¼ .770) and 39% (OR ¼ .610), respectively.
Finally, youth who had a psychological evaluation were more than 5 times more likely (OR ¼ 5.189)
to receive treatment services, and high risk youth were 3.6% (OR ¼ 1.036) more likely to receive
treatment services. Many of these findings are in expected directions and consistent with prior

research, which will be explored in greater depth in the discussion.

Source of

Funding

The second dependent variable examined was the source of funding for the treatment services

youth on probation received, particularly whether certain characteristics of youth influence

whether they receive treatment services through external funding, exclusively, compared to

court-based funding. The bivariate results comparing the three funding sources are presented

White 71

Table 2. Bivariate Statistics—

Youth Receiving Treatment Services.

Variables
No Treatment Services Treatment Services

% %

Gender
Female (reference) 76.1 23.9
Male 74.8 25.2

Race/ethnicity*
White (reference) 73.2 26.8
Black 73.6 26.4
Latino 77.4 22.6
Native American 71.4 28.6
Other 80.0 20.0

Age (mean, SD)*** 16.2 (0.02) 15.6 (0.04)
Living situation***

Single parent (reference) 78.2 21.8
Two parents 80.0 20.0
Grandparent or other family 69.3 30.7
Other-DCS 53.5 46.5

School enrollmenty

Enrolled in school (reference) 74.3 25.7
Not enrolled in school 77.2 22.8

Offense severity***
Property felony (reference) 78.0 22.0
Personal felony 59.7 40.3
Property misdemeanor 82.2 17.8
Personal misdemeanor 73.7 26.3
Drugs 78.7 21.3
Public peace 78.9 21.1
Other 82.0 18.0

Preadjudication detention***
Not detained (reference) 77.0 23.0
Detained 72.2 27.8

Prior referral
No prior referrals (reference) 74.1 25.9
Prior referral 74.5 24.5

Prior adjudication***
No prior adjudications (reference) 73.9 26.1
Prior adjudications 82.5 17.5

Psychological evaluation***
No psychological evaluation (reference) 87.9 12.1
Psychological evaluation 53.5 46.5

Prior treatment service**
No prior treatment services (reference) 75.8 24.2
Prior treatment services 71.5 28.5

Risk level***
Low (reference) 78.4 21.6
Moderate 77.3 22.7
High 72.7 27.3

N 2,835 944

Note. N ¼ 3,779. Continuous measures were examined using a t-test and categorical variables were examined using a w2 test.
SD ¼ standard deviation; DCS ¼ Department of Child Services.
*p � .05. **p � .01.***p � .001. yp � .1.

72 Youth Violence and Juvenile Justice 17(1)

in Table 4. Regarding statistically significant differences across court-based and external funding,

race/ethnicity, living situation, offense severity, preadjudication detention, psychological evalua-

tion, risk level, and the type of treatment service had a statistically significant relationship with the

source of funding for the treatment services. In regard to race and ethnicity, Native Americans in

particular were more likely to get external funding (70%), whereas the other groups were more
similar in the use of external funding.

One of the most notable differences between the two sources of funding was the youth’s living

situation. Roughly 75% of youth who lived with grandparents or other family, over 80% of youth
who live with two parents, and almost 90% of youth living with one parent received funding through
the court, whereas 67% of youth who lived in other living situations such as State were funded
externally. Regarding preadjudication detention, youth who were detained were more likely to

receive treatment services through external funding rather than court based. Youth who had a

psychological evaluation were more likely to receive treatment services via external funding,

whereas youth who did not have a psychological evaluation were more likely to have their treatment

services funded by the court. Low risk youth were also more likely to receive court-based funding

for treatment services.

Table 3. Logistic Regression Predicting Youth Receiving Treatment Services.

Variables b SE Exp(b)

Male 0.106 .109 —
Age �0.226*** .033 0.797
Race/ethnicity

Black �0.407** .130 0.666
Latino(a) �0.247** .097 0.781
Native American �0.088 .207 —
Other �0.299 .367 —

Living situation
Two parents 0.029 .114 —
Grandparents or relatives 0.296* .147 1.345
DCS and other 0.709*** .126 2.032
Not enrolled in school

y �0.129 .105 —
Offense severity

Felony person 0.601*** .125 1.824
Misdemeanor property �0.289 .162 —
Misdemeanor person 0.014 .172 —
Drugs 0.042 .137 —
Public peace 0.175 .145 —
Other �0.116 .363 —

Preadjudication detention �0.261** .095 0.770
Prior referral �0.121 .121 —
Prior adjudication �0.494*** .144 0.610
Psychological evaluation 1.647*** .092 5.189
Prior treatment service 0.131 .111 —
Risk level

Moderate 0.035 .137 —
High 0.351* .149 1.036
Constant 1.585** .543 —

Log-likelihood �1,754.29
Pseudo R2 .1748

Note. N ¼ 3,779. DCS ¼ Department of Child Services; SE ¼ standard error.
*p � .05. **p � .01.***p � .001. yp � .1.

White 73

Table 4. Bivariate Statistics—Source of Funding for Treatment Services.

Variables
Court Based

%
External

%

Gender
y

Female 66.4 33.6
Male 73.5 26.5

Race/ethnicity***
White (reference) 76.3 23.7
Black 68.8 31.2
Latino 75.2 24.8
Native American 30.0 70.0
Other 54.5 45.5

Age—mean (SD) 15.7 (0.05) 15.3 (0.09)
Living with***

Two parents (reference) 80.8 19.2
Single parent 89.9 10.1
Grandparent or other family 74.7 25.3
Other-DCS 33.1 66.9

School enrollment
Enrolled in school (reference) 71.4 28.6
Not enrolled in school 74.9 25.1

Offense severity***
Property felony(reference) 73.0 27.0
Personal felony 70.9 29.1
Property misdemeanor 58.2 41.8
Personal misdemeanor 58.7 41.3
Drugs 78.1 21.9
Public peace 86.3 13.7
Other 80.0 20.0

Preadjudication detention**
Not detained (reference) 76.4 23.6
Detained 67.1 32.9

Prior referraly
No prior referrals (reference) 76.2 23.8
Prior referral 70.2 29.8

Prior adjudication
No prior adjudications (reference) 72.3 27.7
Prior adjudications 71.4 28.6

Psychological evaluation**
No psychological evaluation (reference) 79.1 20.9
Psychological evaluation 69.0 31.0

Prior treatment servicey

No prior treatment services (reference) 73.6 26.4
Prior treatment services 67.2 32.8

Risk level***
Low (reference) 86.3 13.7
Moderate 68.2 31.8
High 69.6 30.4

Exclusive type of treatment service***
GMH outpatient (reference) 65.1 34.9
GMH residential 19.6 80.4
Sex offender outpatient 85.4 14.6

(continued)

74 Youth Violence and Juvenile Justice 17(1)

Finally, there were differences across the type of treatment service the youth received and the

funding source for those treatment services. In general, outpatient treatment services were more

likely to be funded by the court, while residential services (GMH, sex offender, and substance abuse)

were more likely to be funded by external sources. These findings indicate that both characteristics

of the youth and the type of treatment service required by the court are related to the source of

funding used to pay for treatment services.

The results from a two-stage FIML probit model predicting external funding compared to court-

based funding are presented in Table 5.
8

The results from the analysis show that Native Americans

are 76.5% (OR ¼ 1.765) more likely to receive treatment services through external funding, which is
likely due to their tribal health care. Youth who were living in state care, such as DCS, were over 2

times (OR ¼ 2.07) more likely to receive treatment services through external funding sources. Youth
who committed personal felonies and public peace offenses were 32.4% (OR ¼ .676) and 46.3%
(OR ¼ .537), respectively, less likely to receive treatment services through external funding. Pre-
adjudication detention and moderate-risk level had a positive significant effect, indicating that youth

who were detained prior to adjudication and youth who were moderate-risk level are more likely to

receive treatment services via external funding. In regard to psychological evaluation, youth who

received a psychological evaluation were less likely to receive services through external funding.

Finally, to address the second part of the research question—certain treatment services were more

likely to be funded by external sources, while other services were less likely, after controlling for

individual covariates. Specifically, GMH and substance abuse residential services were more likely to

be funded by external funding sources, whereas behavior-specific education, evidence-based, and drug

court services were more likely to be funded by the court. Lastly, youth who received two services or

three or more services were less likely to receive their services through external funding sources.

Discussion

The current study examined the receipt and funding of treatment services for mental and behavioral

problems among a sample of youth under probation supervision. Over the last two decades, research-

ers and practitioners have started to examine mental and behavioral service needs of youth and gain a

better understanding of the complexities of providing treatment services in the juvenile justice

system. Given this context, the current study contributes to the larger body of research on juvenile

justice and treatment services by (1) examining the actual receipt or use of treatment services by

youth under probation supervision, rather than referrals for services, and (2) examining the source of

funding for treatment services. In light of the significant findings presented in the previous section,

there are a number of key findings: (1) Few youth overall receive treatment services while on

Table 4. (continued)

Variables
Court Based
%
External
%

Sex offender residential 45.1 54.9
Substance abuse outpatient 85.3 14.7
Substance abuse residential 9.1 90.9
Other services 97.9 2.1
Two services 86.7 13.3
Three or more services 92.5 7.5

Note. n ¼ 861. Continuous measures were examined using a t test and categorical variables were examined using a w2 test.
MH ¼ general mental health; SD ¼ standard deviation; DCS ¼ Department of Child Services.
*p � .05. **p � .01. ***p � .001. yp � .1.

White 75

probation, (2) there are racial disparities in the receipt of treatment services, and (3) a disconnect

exists between receiving treatment services and the willingness or capability of external funding

sources to fund these services. These findings deserve further elaboration in the broader context of

research and implications for practice and policy.

Table 5. Stage-Two FIML Probit Model Predicting External Funding for Treatment Services.a

Variables b SE Exp(b)

Male 0.239 .161 —
Age �0.084 .069 —
Race/ethnicity

Black 0.076 .151 —
Latino(a) 0.141 .111 —
Native American 0.568* .264 1.765
Other 0.299 .438 —

Living situation
Two parents �0.285y .171 —
Grandparents or relatives 0.121 .184 —
DCS and other 0.727** .265 2.069
Not enrolled in school 0.097 .121 —

Offense severity
Felony person �0.391** .148 0.676
Misdemeanor property 0.294 .181 —
Misdemeanor person �0.100 .193 —
Drugs �0.097 .159 —
Public peace �0.621** .230 0.537
Other �0.553 .532 —

Preadjudication detention 0.247** .105 1.280
Prior referral �0.054 .139 —
Prior adjudication �0.009 .204 —
Psychological evaluation �0.559*** .184 0.572
Prior treatment service 0.165 .145 —
Risk level

Moderate 0.442* .199 1.556
High 0.356 .238 —

Exclusive type of treatment serviceb

GMH residential 0.929*** .221 2.532
Sex offender outpatient �0.155 .199 —
Sex offender residential �0.293 .234 —
Substance abuse outpatient �0.251 .174 —
Substance abuse residential 1.235** .397 3.438
Other service �0.822* .325 0.440
Two services �0.599** .208 0.549
Three or more services �0.896** .342 0.408

Constant 1.648* .726 —
Log likelihood �1,983.15
Rho w2 0.98
Model w2 156.5***

Note. N ¼ 861. GMH ¼ general mental health; FIML ¼ full information maximum likelihood; DCS ¼ Department of Child
Services; SE ¼ standard error.
aStage-one FIML probit model predicted youth receiving any treatment services. bMentoring/life skills services omitted due to
perfect prediction into court-based funding.
*p � .05. **p � .01. ***p � .001. yp � .1.

76 Youth Violence and Juvenile Justice 17(1)

Use of Treatment Services

The first main finding of the current study is that approximately 25% of youth on probation received
treatment services. Estimates of mental health disorders among youth in the juvenile justice system

are as high as 60–70% (Garland et al., 2001; Shufelt & Cocozza, 2006; Teplin et al., 2002), and
roughly half of which also suffer from substance use disorders (Teplin et al., 2002). Given that

almost 40% of the youth received a psychological evaluation (a proxy for mental health problems) in
the current study, it was expected that more youth would receive treatment services. This finding is

consistent with other research that has found a relatively small proportion of youth receive services

in the juvenile justice system despite high prevalence rates (Rogers et al., 2006; Wasserman et al.,

2008), providing additional support that youth with mental and behavioral problems are an under-

served segment of the juvenile justice population.

Unlike much of the prior research, there were no gender differences in service use, but the living

situation did influence the use of treatment services as well as a number of variables related to

offending history and involvement in the juvenile justice system. Particularly youth who lived

without their parents, either with grandparents or other family and especially those is DCS or State

care, were more likely to receive services. Parents and caregivers play an important role in recog-

nizing mental health problems and accessing services to meet service needs (Harrison, McKay, &

Bannon, 2004); therefore, youth not living with parents and entering the juvenile justice system may

have greater unmet service needs that were not being addressed previously. In comparison, youth

living with parents may have more opportunity for support from parents, have fewer service needs,

or may already be receiving services. Additionally, youth under the care of their grandparents or

other relative may have been previously connected to social services agencies and professionals who

may have facilitated services beyond those initiated by correctional service agencies. An alternative

argument is that parents may pose certain obstacles to youth receiving services, such as lack of

involvement (Broeking & Peterson-Badali, 2010; Davies & Davidson, 2001; Peterson-Badali &

Broeking, 2010) or hesitation due to cultural or views about parenting, subsequently affecting

youth’s responsivity to treatment (Haqanee et al., 2015). As a result, courts may be more likely

to refer youth to services when they live with grandparents, other family, or some other care.

Youth convicted of a personal felony and high risk youth were also more likely to receive

treatment services, while youth who were detained or had prior adjudication were less likely to

receive services. In some ways, these are conflicting results; on one hand, it reflects that youth with

more need (not living with parents, felony, and high risk) are getting services, but those previously

detained or adjudicated are not as likely to receive services. This finding may reflect the court

focusing services on youth with high need and limited involvement in the juvenile justice system,

while the court may be more apprehensive to provide treatment services to repeat offenders because

it is viewed as not effective or a good use of resources.

Importantly, a psychological evaluation was a strong predictor of receiving treatment, but

there were still many youth who had an evaluation but did not receive services. Returning to the

RNR model and the importance of identifying risk and needs, and matching services to those

needs, this finding is consistent with research that has found identified needs are not always met

with the appropriate services, often due to lack of resources and programming (Gebo, Stracuzzi,

& Hurst, 2006; Shook & Sarri, 2007), experience with RNR assessments (C. Schwalbe, 2004), or

prioritizing other needs that may not qualify as a risk/need according to assessment tools (Bonta,

Rugge, Scott, Bourgon, & Yessine, 2008; Haqanee et al., 2015; Young, Moline, Farrell, & Bierie,

2006). In the current study, a small percentage (6.6% of youth receiving services) received
evidence-based programs, which is a fraction of all the youth on probation, despite a large

number of youth classified as high risk, suggesting a disconnect between risk/needs and use of

evidence-based programming.

White 77

While support for RNR assessment tools and success in reducing risk of recidivism is evident, the

complexities of youths’ risks and needs create many challenges for implementation in the justice

system, particularly given the inconsistent relationship between mental health and recidivism

(Bonta, Blais, & Wilson, 2014; Wibbelink et al., 2017). If treating mental health problems does

not reduce recidivism, the juvenile justice system may not prioritize it as a need worth addressing.

On the other side, mental health problems are considered in the responsivity principle and problems

that interfere with or limit engagement in criminogenic need-focused intervention are prioritized for

service. For instance, mental health problems may increase an individual’s vulnerability to crimino-

genic needs, such as when a mental health issue interferes with school performance or behavior or

when mental health issues contribute to family conflict. More research is needed to untangle some of

these nuances and complexities to provide clearer goals for the justice system in treating mental health

problems. The current study did not have diagnostic information from youths’ case files, and as a

result, the type of emotional or behavioral problem, the severity of the problem, history of substance

abuse, and comorbidity with other disorders could not be determined, making it difficult to truly assess

the level of service needs of these youth and whether the services are addressing those need.

Another key finding in the current study is the presence of racial and ethnic disparities in the receipt

of treatment services by youth while on probation. Prior research has found that minorities are more

likely to have unmet service needs compared to White youth (Alegria, Carson, Goncalves, & Keefe,

2011; Angold et al., 2002; Garland et al., 2005; Kataoka et al., 2002; Thompson, 2005; Yeh et al.,

2003), and when they do receive treatment services, it is more likely to occur in the juvenile justice

system rather than the mental health system (Atkins et al., 1999; Cohen et al., 1990; Dembo et al.,

1994; Evens & Stoep, 1997; Thomas & Stubbe, 1996). The current study found that among youth on

probation, Blacks and Latinos were less likely to receive treatment services than their White counter-

parts, after controlling for other youth and behavioral characteristics. Therefore, even though the

juvenile justice system may be their best opportunity to receive treatment services (Rawal et al.,

2004), minorities remain less likely to receive treatment services while under probation supervision.

This finding can be understood in the larger context of health disparities and access to health care.

It is well-established that minorities, particularly Blacks, have poorer health which can be attributed

to a number of factors such as low-socioeconomic status and limited access to quality health care

(Center for Disease Control and Prevention, 2013). Additionally, racial and ethnic minorities have

limited access to services, needs are more likely to go unmet, and when services are received they

are of poor quality (Atdjian & Vega, 2005; McGuire & Miranda, 2008; Snowden, 2001; U.S.

Department of Health and Human Services, 2001; Williams, 2005). These disparities have been

attributed to limited access to treatment and health-care providers geographically and financially

(Alegria et al., 2006; Simpson et al., 2005) as well as the mistrust of beneficial services and stigma

associated with receiving mental health services inhibiting minorities in particular from seeking

treatment services (U.S. Department of Health and Humans Services, 2001).

The disparate access to treatment services in the juvenile justice system may stem from multiple

sources, including the identification and diagnosis of mental health and substance abuse disorders

through common psychological evaluations and diagnostic instruments that have been criticized for

their use on youth and minorities (Grisso, 2004). For example, diagnoses are not sensitive to contextual

differences because disorders are identified based on the presence or absence of symptoms but fail to

take into account the developmental relevance to youth or cultural differences (Grisso, 2004; Rogler,

1993; Safran et al., 2009; Smith, Spillane, & Annus, 2006; Wakefield, 1997). As a result, the service

needs of minority youth may not be adequately identified and assessed.

Second, disparities may be the result of stereotyping and biased beliefs about amenability to

treatment. Sentencing research has tested attribution theory (see Albonetti, 1991; Bridges & Steen,

1998) and has found that minorities are treated more harshly in the juvenile justice system because

their behavior is attributed to internal causes or “bad” personality traits, rather than external factors

78 Youth Violence and Juvenile Justice 17(1)

that can be addressed with treatment. These negative stereotypes have also been found in the health

field where doctors believe Blacks are less likely to comply with treatment (McGuire & Miranda,

2008). Similarly, court officials may believe that minority youth are less deserving of treatment

services or that the treatment services will not be as effective or beneficial to minority youth. These

findings support the historical argument that are two juvenile justice systems, one for Whites and

one for Blacks (G. Ward, 2012), where minority youth have a different experience when they enter

the juvenile justice system, characterized by harsh treatment and little access to services. This may

have long-term implications for their involvement in the juvenile and criminal justice systems as

well as perpetuating health differences that continue over the life course (Yazzie, 2011).

Youth with mental and behavioral service needs can be found in multiple “systems of care,”

including the education system, the mental health system, child welfare system, and the juvenile

justice system (Garland et al., 2001; Stroul, 2002; Stroul, Blau, & Sondheimer, 2008). It is essential

that these systems of care collaborate by sharing information and resources to help ensure that

service needs for youth who are vulnerable to mental and behavioral problems are identified as

early as possible and that services are provided. Unmet mental and behavioral service needs in youth

can affect both their success while on probation and their future involvement in the criminal justice

system (Binswanger, Redmond, Steiner, & Hicks, 2012; Kutcher & McDougall, 2009; Yazzie,

2011) as well as other aspects of life like successful employment and healthy relationships.

Funding Sources of Treatment Services

The current research found that a majority (66%) of youth who received treatment services were
funded by the court, and most of the youth who receive funding for treatment services through

external funding sources, through AHCCCS or RBHA, as well as tribal health coverage. Very few

youth received treatment services through private insurance, which was not unexpected because

private insurance companies often have a disclaimer that the insurance company is not required to

cover court-ordered services, unless medically necessary. Given the socioeconomic status of youth

in the juvenile justice system, it was expected that more youth would have external funding for

services through public assistance like AHCCCS. It may be that in some instances, the court is

having to fund services of youth with private insurance who are not eligible for public assistance, but

insurance will not cover the services. Unfortunately, the current study was not able to capture

whether the youth had health coverage prior to their involvement in the juvenile justice system,

or the type of health insurance, so it is difficult to assess the role of prior health coverage, and

whether the court still funded the treatment services when a youth had coverage.

These findings are informative for court administrators to better understand the factors related to

youth receiving treatment services through external funding compared to the youth who tend to

receive services via court-based funding, which has implications for the continuity of care. Partic-

ularly, treatment services may be beneficial to youth after their involvement in the juvenile justice

system, but without court-based funding, the services cannot be continued unless the youth is able to

attain other sources to cover the cost of the services. If the youth is eligible for Medicaid to cover

services, there may be a change in service provider and any established rapport with a mental health

professional is disrupted. The process of continuing care after probation has ended may be less

disruptive if the services are funded through external sources from the beginning. Youth may be able

to continue using the same service provider with the same health care coverage. Youth who received

psychological evaluations were more likely to have services funded by the court, which is likely

because psychological evaluations are funded by the court so continuity of services is more likely if

the same service provider and funding source is used by the court.

If more youth become insured and behavioral health services covered to a greater extent as a result

of health-care reform (Cockburn, Heller, & Sayegh, 2013; Council of State Governments Justice

White 79

Center, 2013), we may see more services in the juvenile and criminal justice system covered through

external funding sources such as Medicaid or private insurance. Expanding mental health coverage and

Medicaid may shift the burden of funding treatment services off the juvenile justice system and into

the health-care system, allowing the juvenile justice system to focus on the delinquency of youth. This

is consistent with Feld’s (1999) argument that the juvenile justice system should be responsible for

responding to delinquent and criminal behavior and other systems of care should be responsible for the

care and welfare of youth. This reform would require the collaboration of agencies to work together

and share information regarding the service needs of youth to help them be successful while involved

in the juvenile justice system and ensure treatment services are provided (Clark & Gehshan, 2006).

There also needs to be clarity in the roles of different systems of care or agencies and implicit

guidelines for responding to delinquency and youth experiencing emotional and behavioral problems.

Limitations

This study had the benefits of a large, representative sample of youth on probation, capturing the actual

use of treatment service, and included a number of variables on prior delinquency and involvement in the

juvenile justice system. But the study is not without its limitations. The sample is limited to one county in

the Southwest, so it is not appropriate to generate findings to juvenile justice systems in other jurisdic-

tions. In addition, the data are used for tracking youth and managing files, not for research purposes, so

other measures particularly related to family/home and school/peer life that may impact service decisions

were not captured. Perhaps most importantly, information from psychological evaluations such as mental

health disorder diagnoses was not measured because information in the youth’s case file is typically not

transferred into an electronic form. Without mental health diagnoses, it is difficult to directly measure

service needs. In particular, the type and severity of emotional and behavioral disorders, as well as

the comorbidity of disorders, has important implications for the receipt of treatment services.

Conclusion

There is growing recognition that youth suffer from mental and behavioral problems which affect

multiple aspects of their lives and may put them at risk for delinquency and involvement in the juvenile

justice system. Ideally, the juvenile justice system should be used as a last resort to address these

adversities, but that is not typically the case. Instead, youth enter the juvenile justice system often due

to the absence of viable, community-based alternatives to address the hardships in their lives (Myers &

Farrell, 2008). The overlap in responsibilities for seriously delinquent youth and seriously mentally ill

youth is often labeled as “not ours” (Grisso, 2004), demonstrating the difficultly of serving youth and the

failure of different systems and agencies to take responsibility. The result can be a lifetime of involve-

ment in the criminal justice system (Cocozza & Skowyra, 2000; Davis, Banks, Fisher, & Grudzinskas,

2004; Elliott, Huizinga, & Menard, 1989; Graves, Frabutt, & Shelton, 2007; Pullmann, 2010), which has

been an ongoing struggle for the juvenile justice system and other systems of care (Grisso, 2004, 2008;

Skowyra & Cocozza, 2007). Lipsey, Howell, Kelly, Chapman, and Carver (2010) argued that

the two most progressive policy reforms of recent years are the drive for evidence-based practice, which

focuses on effective treatments, services, and supports for children and families, and the effort to

establish systems of care to address the infrastructure of funding and linkages between services and

programs. (p. 9)

Identifying service needs and providing services matched to those needs is not an easy process, but

the consequences of ignoring the problems can have long-term negative effects both for the indi-

vidual youth and for the larger community.

80 Youth Violence and Juvenile Justice 17(1)

Appendix A

Author’s Note

This study was approved by institutional review board. This article does not contain any studies with human or

animal subjects. Data was de-identified and informed consent was not applicable.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or pub-

lication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

1. For youth with multiple probations, the first probation is included and only the treatment services received

during the probation supervision of interest.

2. Since psychological records were not managed electronically, this limited access to mental health records

and diagnoses.

3. Most often the effective date of treatment services was the start date of probation (mode), but the average time

between probation starting and the start of treatment services was 114 days and the median was 50 days. Therefore,

by including youth that started probation toward the end of my time frame, these youth may not have had an

opportunity to receive treatment services while on probation and would be not captured in my data. As a limitation,

the number of youth receiving treatment services while on probation is likely underestimated to some degree.

4. The amount missing on each variable did not exceed 5% of the entire sample, and the total missing cases was

less than 10% of the sample, listwise deletion was used to deal with the missing data problem in the current

analysis (see Bennett, 2001; Schafer, 1999).

5. Behavior-specific education includes a variety of programs and classes targeted at specific behaviors, such

as anger management, conflict resolution, or shoplifting. Mentoring services involve pairing youth with an

Youth Receiving Treatment Services.

Type of treatment service

All Youth Receiving
Service

Youth Receiving
Service Exclusively

Duration of Treatment
Servicea (days)

n % n % Mean SD Median Range

GMH outpatient 282 29.9 173 18.3 133.7 99.07 92.0 1–591
GMH residential 188 19.9 103 10.9 156.1 108.8 130.5 1–668
Sex offender outpatient 140 14.8 101 10.7 244.5 167.0 202.0 10–804
Sex offender residential 97 10.3 55 5.8 253.2 167.6 206.0 4–718
Substance abuse outpatient 196 20.8 95 10.1 134.8 93.1 90.0 4–633
Substance abuse residential 44 4.7 23 2.4 102.8 58.5 91.5 11–278
Mentoring and life skills 179 19.0 95 10.1 102.6 55.1 90.0 3–391
Behavior specific education 6 0.6 4 0.4 103.3 72.8 90.0 18–238
Evidence-based programs 62 6.6 31 3.3 141.9 80.4 131.0 4–430
Drug court 70 7.4 16 1.7 144.7 88.8 143.0 3–373
Two services — — 188 20.0 — — — —
Three or more services — — 59 6.3 — — — —
Total — — 944 100.0 186.4 141.1 147.0 1–850

Note. n ¼ 944. Mode duration is 90 days for all types of services. GMH ¼ general mental health; SD ¼ standard deviation.
aType of treatment services are not mutually exclusive.

White 81

adult role model improve prosocial development and also include life skills development and comprehensive

youth programs. The evidenced-based programs include Brief Strategic Family Therapy (BSFT), Functional

Family Therapy (FFT), Multi-Systemic Therapy (MST), and Multi-Systemic Therapy for Problem Sexual

Behavior (MST-PSB). For GMH services, sex offender services, and substance abuse services, youth can

receive out-of-home or residential treatment or outpatient care in the community. In addition, youth in the

residential treatment setting can receive these services in the Level I Residential Unlocked unit, the Level I

Residential Locked unit, the Level II Residential unit, and in Department of Economic Security licensed

group homes. Outpatient services include individual counseling, family counseling, group counseling,

home-based counseling, and multifamily group counseling, and therapeutic days.

6. Youth can also receive funding for services from the court and external sources (n ¼ 83), but this group is not
included in the current analysis.

7. Receiving treatment services is not a random process and factors that influence whether a youth receives

treatment services might also influence the type of funding source for treatment services, which constitutes

selection bias. When there is selection bias, the standard errors of the selection model (receiving treatment

services) can be correlated with the standard errors of the primary dependent variable (funding source)

effecting the statistical significance of independent variables on the outcome. The full information maxi-

mum likelihood probit model predicts selection into treatment service to control for selection bias, which is

followed by analyzing the dependent variable of interest in the second stage of the model (Berk, 1983).

8. Stage one predicting treatment services are not presented but are similar to the logistic regression results

presented in Table 3.

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Author Biography

Clair White completed her PhD student at Arizona State University in the School of Criminology

and Criminal Justice in 2015. She is a research assistant professor at the Center for Evidence-Based

Crime Policy at George Mason University. Her research interests include mental health and the

criminal justice system, service use, crime and place, and the illicit use of prescription drugs.

White 87

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The Impact of Victimization and Mental Health Symptoms on Recidivism
for Early System-Involved Juvenile Offenders

Lindsey E. Wylie
University of Nebraska Omaha

Katrina A. Rufino
University of Houston–Downtown and The Menninger Clinic,

Baylor College of Medicine

Although research has linked mental health symptoms and prior victimization to recidivism for youth on
probation or in detention, little attention has been given to these risk factors for early system-involved
youth. We conducted a survival/hazard model to estimate the impact of official records of abuse/neglect,
crime victimization, and mental health issues (mood, anxiety, disruptive, and substance use disorders) on
recidivism in a sample of 2,792 youth in a large Midwestern diversion program. Results indicated that
youth with official records of abuse/neglect, person crime victimization, and property crime victimization
were more likely to recidivate sooner than those without these victimization experiences (hazard ratio:
1.37, 1.42, and 1.52, respectively). Findings from the present study also demonstrated that substance use
disorder was the only mental health cluster that predicted quicker time to recidivism. As one of the
earliest points of entry into the juvenile justice system, diversion programs are in a unique position to
address trauma from multiple types of victimization and adapt diversion programming to be responsive
to each juvenile’s mental health needs.

Public Significance Statement
Early system-involved youth referred to juvenile diversion had high levels of mental health symp-
toms and many had prior experiences with various types of victimization that are based on official
law enforcement records. Prior victimization significantly predicted whether a youth had future
contact with the juvenile or adult criminal justice system, even while considering other factors, such
as risk level and youth characteristics.

Keywords: juvenile recidivism, juvenile diversion, mental health, victimization

In 2016, there were approximately 856,130 juvenile arrests in the
United States—many for nonviolent offenses such as larceny–theft,
other assaults, drug abuse violations, liquor law violations, vandalism,
disorderly conduct, and curfew/loitering (OJJDP, 2016). As such, the
juvenile justice system is often tasked with how to address youth who
commit less serious offenses. One approach is to divert them away
from formal juvenile justice system involvement through diversion
programs. As the gateway to the juvenile justice system, diversion
programs are in a unique position to address the needs of early
system-involved youth, including needs related to victimization and
mental health symptoms, to reduce future involvement in the juvenile
or adult criminal justice system.

Developmental models of antisocial behavior propose that “delin-
quency is marked by a reliable developmental sequence of experi-

ences,” in which childhood experiences and social environment put
children at risk for social maladjustment and criminal behavior (Pat-
terson, Debaryshe, & Ramsey, 1989, p.263). Specifically, studies find
that experiences with victimization, broadly defined as maltreatment,
adverse childhood experiences, and general crime victimization, are
related to mental health issues (e.g., Abram et al., 2004; Kilpatrick et
al., 2000) and that both victimization and mental health issues are
related to juvenile justice involvement (e.g., Barrett, Katsiyannis,
Zhang, & Zhang, 2014; Fazel, Doll, & Långström, 2008). Although
the association of victimization and mental health symptoms within
juvenile justice populations are well-documented, especially within
samples of serious juvenile offenders (e.g., adjudicated or incarcer-
ated), fewer studies have examined these risk factors in a sample of
early system-involved youth. The purpose of this study is to examine
the relationship between prior victimization, as obtained from official
law enforcement records, and mental health symptoms on time to
recidivism in a sample of early system-involved youth in a juvenile
diversion program.

Operationalizing Victimization

Researchers operationalize victimization using multiple defini-
tions. Most studies measure victimization as child maltreatment,
utilizing official data obtained from social service agencies or

This article was published Online First November 1, 2018.
Lindsey E. Wylie, School of Criminology and Criminal Justice, Juvenile

Justice Institute, University of Nebraska Omaha; Katrina A. Rufino, De-
partment of Social Sciences, University of Houston–Downtown and The
Menninger Clinic, Baylor College of Medicine.

Correspondence concerning this article should be addressed to Lindsey
E. Wylie, Juvenile Justice Institute, University of Nebraska Omaha, 941 O
Street, Suite 706, Lincoln, NE 68508. E-mail: slwylie@unomaha.edu

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Law and Human Behavior
© 2018 American Psychological Association 2018, Vol. 42, No. 6,

558

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0147-7307/18/$12.00 http://dx.doi.org/10.1037/lhb0000311

558

mailto:slwylie@unomaha.edu

http://dx.doi.org/10.1037/lhb0000311

child protective services (e.g., Barrett et al., 2014; English, Wi-
dom, & Brandford, 2002; Smith, Ireland, & Thornberry, 2005), or
self-report data obtained from caregivers or youth (e.g., Conrad,
Tolou-Shams, Rizzo, Placella, & Brown, 2014). Other studies
include broader definitions of victimization, usually measured with
self-report data, including adverse childhood experiences (ACEs),
such as abuse/neglect, parental divorce, and family violence (e.g.,
Wolff, Baglivio, & Piquero, 2015; Kilpatrick et al., 2003) or
general crime victimization, such as theft or assault (e.g., Finkel-
hor, Ormrod, & Turner, 2009; Manasse & Ganem, 2009). Research
employing these broader definitions of victimization typically
have not included data from official agency records.

As such, the current study expands previous research using a
broader definition of victimization, to include abuse/neglect, sex-
ual assault, property crimes, and person crimes, utilizing reported
incidents of victimization data obtained from official law enforce-
ment records. Although official records are likely an underestima-
tion of abuse/neglect (Swahn et al., 2006) or general crime trends
(see Loftin & McDowall, 2010) because of failure to report or
other system-wide factors, using this definition has practical im-
plications for programmatic interventions because this information
may be readily available to diversion programs, and may produce
different findings than studies using self-report data.

Victimization and

Mental Health Symptoms

Research demonstrates that victimization as a child or adoles-
cent is associated with later mental health problems in both lon-
gitudinal studies with representative samples (e.g., Finkelhor et al.,
2009; Kilpatrick et al., 2000; Manasse & Ganem, 2009) and
retrospective studies with justice-involved samples (e.g., Barrett et
al., 2014; Dierkhising et al., 2013; Ford, Grasso, Hawke, & Chap-
man, 2013). For instance, in a national random sample of non-
justice-involved children ages 2 to 17, Finkelhor and colleagues
(2009) examined the Developmental Victimization Survey (DVS)
to assess the range of childhood victimizations across five victim-
ization types, including conventional crime (e.g., assaults and
property crimes), child maltreatment, peer and sibling victimiza-
tion, sexual assault, and indirect victimization (e.g., witnessing
violence). Overall, 79.6% of the sample reported lifetime victim-
ization and analysis revealed a strong association between lifetime
poly victimization (the total number of different types of victim-
izations) and mental health symptoms (anger, depression, and
anxiety).

When examining samples of justice-involved youth, a large
proportion of youth report exposure to some type of potentially
traumatic event. Dierkhising and colleagues (2013) reported up to
90% of justice-involved youth experienced one or more potentially
traumatizing events (e.g., traumatic loss, impaired caregiver, do-
mestic violence, school violence), with an average of 4.9 lifetime
events. In measuring the link between victimization and mental
health problems, Ford and colleagues (2013) found that 58% of the
sample endorsed one of the 19 potentially traumatic events (e.g.,
being in a bad accident, witnessing violence, sexual assault), which
was related to posttraumatic stress symptoms, emotional and be-
havioral problems, suicide risk, and alcohol and drug use prob-
lems.

Victimization and Delinquency

Within criminological perspectives and strain theory, Agnew
(1992) argued that criminal victimization is “among the types of
strain that are most likely to lead to delinquency” (Agnew, 1992,
p.306) because it is perceived as unjust and traumatic, which
evokes anger and resentment, and contributes to deviance as a
mechanism to cope with strain (Agnew, 1992; Hay & Evans,
2006). Studies have examined whether victimization increases the
risk for later delinquency, including general delinquency and vio-
lent delinquency, in both representative samples (i.e., longitudinal
studies of nonjustice-involved children) and justice-involved sam-
ples (i.e., retrospective studies of youth who are justice-involved).

Studies including representative samples found that those with a
history of child maltreatment were significantly more likely to
have contact with the police as a juvenile or adult than those
without a history of child maltreatment (Smith & Thornberry,
1995; Smith et al., 2005; Zingraff, Leiter, Myers, & Johnson,
1993). Although the link between maltreatment and delinquency is
consistent across longitudinal studies, the impact of maltreatment
on future violence may be less predictive than other factors,
including antisocial peers, substance abuse, and family socioeco-
nomic status (Hawkins et al., 2000). Furthermore, research dem-
onstrates that general crime victimization is associated with delin-
quency. For example, using a measure of general crime
victimization that included theft, assault, parental physical abuse,
attack by a weapon, and property damage, Manasse and Ganem
(2009) found that for every one point on their victimization mea-
sure, the odds of engaging in delinquency increased by 19%.

Victimization also impacts future reoffending after initial justice
involvement. Wolff and colleagues (2015) tested whether exposure
to ACEs, measured using a sum of 10 binary absence/presence
indicators, significantly predicted time to recidivism following
community-based treatment. Overall, having a greater number of
ACEs was related to a shorter time to recidivism, even while
controlling for youth demographics, and risk factors such as sub-
stance abuse, criminal history, deviant peers, and school behavior.
Others found that gender may also moderate the relationship
between victimization and delinquency. Specifically examining
the association between prior sexual abuse and delinquency in a
sample of 454 juveniles referred by a judge for a mental health
evaluation, Conrad and colleagues (2014) indicated that being
sexually abused as a child was the strongest predictor of recidivism
for girls but not boys, even while controlling for prior legal
involvement and conduct problems.

Mental Health Symptoms

There is growing attention on the high prevalence of mental
health problems in the juvenile justice system and researchers have
consistently found higher rates of mental health problems in
justice-involved youth, than youth in the general population
(Abram et al., 2004; Dierkhising et al., 2013; Fazel et al., 2008;
Teplin, Abram, McClelland, Dulcan, & Mericle, 2002). A meta-
analysis by Fazel and colleagues (2008) examined the results of 25
studies that included interviews with detained juveniles and found
that juveniles in detention and correctional facilities were signifi-
cantly more likely to have mental health disorders (conduct dis-
order, psychosis, attention-deficit/hyperactivity disorder, and ma-
jor depression) than age-equivalent juveniles in the general

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559VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

population. Overall prevalence rates, however, appear to differ by
gender. In a large random sample of youth interviewed during
detention intake, Teplin and colleagues (2002) estimated that ap-
proximately two thirds of males and three quarters of females met
criteria for one or more psychiatric disorders, including disruptive
disorders (attention deficit disorders, oppositional defiant disor-
der), substance use disorders, and affective disorders.

Although the high prevalence of mental health issues within
justice-involved youth has been well-documented, previous re-
search has been mixed with respect to whether mental health is
predictive of reoffending, and if so, what types of disorders may be
most predictive. Cottle, Lee, and Heilbrun’s (2001) meta-analysis
of 23 studies that measured recidivism in juveniles indicated that
conduct problems (e.g., conduct disorder) and nonsevere psycho-
pathology (e.g., stress, anxiety) elevated the risk for recidivism,
but severe psychopathology (e.g., psychosis, suicidality) and a
history of psychiatric treatment did not increase risk for recidi-
vism. On the other hand, in a study of youth referred to probation,
the results indicated that while anxiety and mood disorders did not
relate to recidivism, both substance use disorders and disruptive
behaviors did increase the risk of recidivism (McReynolds,
Schwalbe, & Wasserman, 2010).

Trauma-exposed youth who are justice-involved are at risk for
several mental health disorders, including posttraumatic stress
disorder, major depressive disorder, and substance abuse (Abram
et al., 2004; Dierkhising et al., 2013; Ford, Hartman, Hawke, &
Chapman, 2008). When testing the role of mental health in the
victimization-delinquency link, participants who endorsed depres-
sive symptoms were more likely to respond to general crime
victimization (i.e., theft, assault, parental physical abuse, attack by
a weapon, and property damage) with delinquent behavior. There
was a moderating effect of gender, such that males who experi-
enced depressive symptoms were 50 times more likely to respond
to victimization with delinquency than males without depressive
symptoms; but there were no differences for females with depres-
sive symptoms (Manasse & Ganem, 2009).

A limitation of these studies, however, is that they only provide
support for an association between mental health problems and
risk of delinquency, but do not explain the mechanism by which
mental health problems increase this risk. Recent research has
sought to “disentangle shared risk” to evaluate whether having a
mental health problem explained delinquency outcomes above
criminogenic risk factors and whether mental health issues mod-
erate the relationship between criminogenic risk and delinquency
outcomes (Guebert & Olver, 2014; Schubert, Mulvey, & Glasheen,
2011). These studies demonstrated that even though the presence
of mental health problems was related to reoffending in bivariate
analyses, after controlling for criminogenic risk markers and de-
mographics, mental health issues did not uniquely contribute to
reoffending above these other factors (Guebert & Olver, 2014;
Schubert et al., 2011).

The Current Study

The present study utilized a sample of early system-involved
youth referred to a juvenile diversion program in a large Midwest-
ern city. The purpose of this study was to examine reoffending for
youth with reported experiences of victimization, as well as mental
health symptoms at the time of diversion intake. Although research

has examined the recidivism trajectory of youth at the deeper end
of the juvenile justice system, fewer studies have linked victim-
ization and mental health problems to recidivism in a sample of
early system-involved youth. Juveniles in the diversion program
are typically first-time offenders referred because of minor of-
fenses (e.g., shoplifting, possession of marijuana, status offenses)
and assessed as low to moderate risk. The present research con-
tributes to the larger body of literature by examining whether the
association between victimization, mental health problems, and
recidivism is similar for early system-involved youth to better
inform diversion efforts. Furthermore, the present study extends
prior research by including a broader measure of victimization that
includes abuse/neglect, sexual assault, property crime, and person
crimes that have been reported to law enforcement.

Method

Participants

Participants included 2,792 justice-involved juveniles referred
for diversion in a large Midwestern city. The mean age was 15.08
(SD � 1.64) and the majority were male (59.7%, n � 1,668).
Approximately half identified as White (48.4%, n � 1,352), fol-
lowed by Black (34.1%, n � 953), Hispanic/Latino (14.6%, n �
407), Asian/Pacific Islander (1.1%, n � 31), Native American/
Alaskan Native (1.1%, n � 31), and other or multiple races (0.6%,
n � 17). Most participants were referred to diversion for drug or
alcohol-related offenses such as possession of marijuana or para-
phernalia (35.2%, n � 984) and property offenses, such as shop-
lifting and theft (35.5%, n � 990). Other offenses included disor-
derly conduct (11.7%, n � 326), crimes against others such as
third-degree assault (9.5%, n � 265), traffic offenses such as
driving without a license (2.5%, n � 69), and other offenses such
as vandalism, curfew violation, providing false information to the
police, and obstructing an officer (5.7%, n � 158). Although youth
may be referred to diversion for truancy in this county, this sample
does not include those youth, because truancy diversion is a
separate program that utilizes a different assessment process. If
youth successfully complete the diversion program, the county
attorney does not file their case and they are not adjudicated
delinquent.

Study Design and Procedure

Data were obtained from the juvenile diversion program’s case
management system as part of a statutorily required statewide
evaluation of juvenile justice-related programs that receive fund-
ing from the state. The data included identifying information (e.g.,
name and date of birth) so that we could compute recidivism, as
required under statute for the statewide program evaluation. Insti-
tutional review board approval was obtained by the University of
Nebraska Medical Center as part of the program evaluation.

Between July 1, 2012 and June 30, 2015, a total of 3,934
juveniles were referred to the juvenile assessment center for pos-
sible participation in juvenile diversion following a law violation.
Once a juvenile receives a law violation, if eligible based on
evidentiary factors and the type of offense, the county attorney will
refer the case to the assessment center to determine whether the
youth should participate in the diversion program. Of the youth

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560 WYLIE AND RUFINO

referred during this time, 2,792 were eligible and decided to
participate in diversion, which is the total sample for the study.
Youth may be referred, but not enroll for various reasons includ-
ing: receiving a warning letter if they are screened as lower risk,
they were deemed not eligible by the diversion program, the youth
or family refused to participate, or procedural reasons (e.g., out of
jurisdiction, recommend nolle pros, the youth received a new
charge while awaiting assessment, or the county attorney withdrew
the referral).

During the assessment process, each juvenile completes several
assessments and the assessment specialist creates a diversion plan
based on each juvenile’s risk and needs. If the youth successfully
completes the diversion plan, their case is dismissed and not filed
in juvenile court. If a youth is not successful, either because he or
she did not complete the diversion plan requirements or receives a
new law violation while on diversion, then the case is filed and the
youth goes through the traditional juvenile court process.

Measures

Demographics. We included age, gender, and race/ethnicity
as demographic controls. Age was measured continuously as the
youth’s age at the time of referral. Gender was dichotomous (0 �
male, 1 � female). Race/ethnicity was measured using a dichot-
omous variable with 0 � White and 1 � non-White.

Successful completion of diversion. To control for success-
ful completion of the diversion program, which may influence
future reoffending, we included a dichotomous variable for suc-
cessful discharge (0) or unsuccessful discharge (1) as measured
within the juvenile diversion program’s case management system.
Overall, 83.5% of youth successfully completed their diversion
program requirements.

Risk-level. To measure each juvenile’s level of risk, the
Youth Level of Service/Case Management Inventory 2.0 (YLS/
CMI 2.0; Hoge & Andrews, 2011) was used. The YLS/CMI 2.0 is
a 42-item checklist designed to be completed by a mental health
professional or a probation officer utilizing interviews and/or
record reviews. The measure provides a total score and a score for
each of eight subscales including: Offense History, Family, Edu-
cation, Substance Abuse, Leisure/Recreation, Peer Relations, Per-
sonality/Behavior, and Attitudes/Orientation. Each item is coded
as present or absent for a total score ranging from 0 to 42. These
total scores determine if the juvenile is low (0 through 8), moderate
(9 through 22), high (22 through 34), or very high (35 through 42)
risk for recidivism. The information for each item is gathered from
the youth and family directly, and through collateral information
(e.g., the school, other agencies). The assessment tool is for youth
aged 12 to 18 years old, therefore youth in the sample younger than
12 were not assessed via the YLS/CMI. The manual provides
evidence of strong reliability and validity (Hoge & Andrews,
2011) and was validated elsewhere (Onifade et al., 2008; Vincent,
Guy, & Grisso, 2012). On average, the sample was a low to
moderate risk group with a mean YLS/CMI-total score of 7.46
(SD � 4.21). Most of the sample scored in the low risk range
(65.7%) or moderate risk range (34.2%), with only three partici-
pants scoring in the high-risk range (0.1%) and none in the very
high-risk range.

Official records of victimization. As part of the diversion
program’s case management system, data is automatically pulled

from law enforcement any time the youth is listed as a victim. As
such, the measure of victimization includes only reported incidents
that involved law enforcement. Each incident of victimization was
recoded into one of four categories: sexual assault, abuse/neglect,
property crime (e.g., burglary, theft, robbery) victimization, and
person crime (e.g., assault) victimization. Property and person
crimes were classified based on the FBI Uniform Crime Reporting
Program (U.S. Department of Justice, 2016), with person crimes
including those where the individual is the direct victim and
property crimes including instances where the object is to obtain
money or some other benefit. Overall, 16.3% of the sample had at
least one occurrence of reported victimization, 1.9% had two
occurrences, and 0.3% had three occurrences of reported victim-
ization. The prevalence of each victimization type was: abuse/
neglect (6.9%), sexual assault (1.7%), victim of a person crime
(9.9%), and victim of a property crime (2.6%).

Mental health symptoms. The DISC Predictive Scales (DPS;
Lucas et al., 2001) was used to measure each juvenile’s presenting
symptoms at the time of their assessment. The DPS was developed
as an efficient diagnostic screening tool for juveniles and identifies
youth who are highly likely to meet diagnostic criteria (McReyn-
olds, Wasserman, Fisher, & Lucas, 2007). The DPS was validated
in a community and mixed sample of “troubled” 10- to 18-year-old
youth of both genders (Lucas et al., 2001) and with justice-
involved juveniles (McReynolds et al., 2007). The DPS is a com-
puterized self-report tool that uses audio to read each question. The
number of questions for each module varies depending on the
rule-out criteria and follow-up questions based on flagged re-
sponses. The DPS derives from the most sensitive questions con-
tained in the Diagnostic Interview Schedule for Children-2.3
(DISC; Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000) to
determine if symptoms are “present,” “possible,” or “absent”
within the last year.

For the purposes of these analyses, we combined “possible” and
“absent” for a dichotomous measure of each symptom as either
present (1) or possible/absent (0). For each of the symptoms, we
organized them into four clusters similar to previous studies
(McReynolds et al., 2007, 2010; Wasserman, McReynolds, Lucas,
Fisher, & Santos, 2002). The clusters utilized in the analyses were:
disruptive behaviors (attention deficit hyperactivity, oppositional
defiant, and conduct disorder), substance use (alcohol use, mari-
juana use, and other substance use), anxiety (posttraumatic stress
disorder, agoraphobia, social phobia, general anxiety, obsessive–
compulsive disorder, specific phobia, and panic disorder), and
mood (depression and mania). Overall, 63.8% of the juveniles in
this sample endorsed one or more mental health symptom clusters:
41% endorsing the disruptive cluster, 35.3% the anxiety cluster,
21.7% the substance use cluster, and 19.6% the mood cluster.

Recidivism. Recidivism data were obtained from the state’s
trial court case management system and was defined as any of-
fense that was filed in court following discharge from diversion,
excluding cases that were eventually dismissed. Data included all
juvenile and adult misdemeanor and felony cases between July 1,
2012 and December 31, 2015, including sealed records. These
dates allowed at least a 6-month recidivism period for juveniles
enrolling in diversion at the end of the study period (June 30,
2015). The time at risk from discharge to the end of the study
period ranged from 180 days to 1,271 days, with a mean of 850.83
days (SD � 389.60 days, Mdn � 859.00 days).

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561VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

Using probabilistic record linkage software, we matched youth
in the sample to the recidivism records using first name, middle
name, last name, and date of birth, which accommodates mis-
spelled names or typos with dates of birth. We included any
offenses, including status offenses, that may bring a youth back in
to the juvenile justice system or diversion, which in this state,
includes truancy offenses eligible to be filed on in court (20 or
more absences that are not medically related). Of those who
recidivated (n � 839, 30.1%), the most common offenses included
drug or alcohol-related offenses (n � 255, 30.4%) and property
offenses (n � 229, 27.3%); the remaining offenses included dis-
orderly conduct (7.2%, n � 60), crimes against others such as
assault and robbery (7.7%, n � 67), traffic offenses such as driving
without a license (2.5%, n � 21), truancy (10.7%, n � 90), and
other offenses such as vandalism, curfew violation, providing false
information to the police, and obstructing an officer (13.9%, n �
117).

Data Analysis and Hypotheses

First, several bivariate analyses were conducted to compare
juveniles who endorsed victimization and those who did not. The
victimization types, gender, and reason for discharge were com-
pared to each other and with each mental health cluster using a
chi-square test due to the dichotomous nature of the variables.
Differences for gender and victimization on age and risk level
were compared using analysis of variance. To test the relationship
of the independent variables while controlling for demographic
factors and risk level, data were analyzed using a hazard Cox
regression analysis to predict time to failure (i.e., recidivating
event), which provides more details than a simple dichotomous
indicator of recidivism because it specifically targets the amount of
time to reoffending (Wolff et al., 2015). Prior research has indi-
cated that the time to the recidivating event is an important
outcome to consider because the individual characteristics of ju-
veniles who recidivate sooner may be different than the charac-
teristics of juveniles who recidivate later (Maltz, 1984; Schmidt &
Witte, 1989).

The model specifically included the four victimization types and
the four mental health symptom clusters across gender and race/
ethnicity, while controlling for age, risk level, and successful
discharge. For the purposes of the final multivariate model, we
removed the Substance Abuse subscale from our measure of risk
level because of the overlap between the YLS/CMI Substance Use
subscale and the mental health substance use cluster measured
with the DISC Predictive Scales. Preliminary analyses confirmed
the overlap between the YLS/CMI-total and the substance use
mental health cluster. Specifically, when including the YLS/CMI-
total and the substance use mental health cluster in the model, the
substance use mental health cluster was not significant; however,
when including the modified risk level score without the Substance
Use subscale, the substance use cluster remained a significant
predictor of time to recidivism. As such, the measure of risk level
in the multivariate model includes the modified YLS/CMI without
the Substance Use subscale and is identified as the modified risk
score in analyses.

On the basis of previous research on the role of victimization in
juvenile delinquency, we hypothesized that juveniles who had
been victimized would recidivate sooner than juveniles who had

not been victimized (Wolff et al., 2015) for all four types of
victimization. Consistent with previous research on the relation-
ship between mental health symptoms on offending and recidi-
vism, we also hypothesized that mental health issues would predict
recidivism at the bivariate level, especially for the disruptive
behavior and substance use clusters (McReynolds et al., 2010;
Vermeiren, Schwab-Stone, Ruchkin, De Clippele, & Deboutte,
2002); however, it was hypothesized that once we controlled for
other factors such as risk level, then mental health symptoms
would be less predictive (Guebert & Olver, 2014; Schubert et al.,
2011).

Results

Bivariate Relationships

First, we compared the bivariate relationship of those who had
experienced a reported incident of victimization, compared with
those who had not, to gender, race/ethnicity, and discharge reason
for each victimization type (see Table 1). With respect to gender,
the only victimization type that was significantly different by
gender was sexual assault, �2(1) � 37.68, p � .001, as females
have 4.9 times greater odds of reporting sexual assault than males.
There were no gender differences for abuse/neglect, �2(1) � 0.17,
p � .68, property crime, �2(1) � 0.94, p � .33, or person crime,
�2(1) � 0.08, p � .79. There were differences by race/ethnicity
across three of the four victimization types. Although White youth
were 1.71 times more likely to report property victimization inci-
dents than non-White youth, �2(1) � 4.75, p � .029, non-White
youth were 1.63 times more likely to report victimization incidents
of person crimes, �2(1) � 14.20, p � .001, and 1.92 times more
likely to report abuse/neglect, �2(1) � 17.57, p � .001, than White
youth. There were no differences for race/ethnicity and sexual
assault, �2(1) � 1.53, p � .22. For discharge reason, youth with a
reported instance of abuse/neglect, �2(1) � 13.46, p � .001, were
1.87 times more likely to be unsuccessfully discharged than those
without a reported abuse/neglect incident. Those with a reported
instance of a person crime, �2(1) � 18.68, p � .001, were 1.08
times more likely to be unsuccessfully discharged than those
without a reported instance of a person crime. There were no
differences for discharge reason for sexual assault victimization,
�2(1) � 0.17, p � .68, or property crime, �2(1) � 3.82, p � .05.

Similar bivariate comparisons were conducted for the mental
health symptom clusters. Female youth were 2.93 times more
likely to endorse the anxiety cluster, �2(1) � 165.89, p � .001,
3.57 times more likely to endorse the mood cluster, �2(1) �
163.80, p � .001, and 1.47 times more likely to endorse the
disruptive cluster, �2(1) � 22.99, p � .001, than male youth;
however, there were no differences for the substance use cluster,
�2(1) � 3.18, p � .08. For race/ethnicity, there were differences
between White and non-White youth for the disruptive cluster,
�2(1) � 30.02, p � .001, substance use cluster, �2(1) � 88.81, p �
.001, and anxiety cluster, �2(1) � 22.56, p � .001. More specif-
ically, White youth were 2.49 times more likely to endorse sub-
stance use symptoms than non-White youth, and non-White youth
were 1.55 times more likely to endorse disruptive disorder symp-
toms and 1.48 times more likely to endorse anxiety symptoms than
White youth. There were no racial/ethnic differences for the mood
cluster, �2(1) � 0.23, p � .63. With respect to successfully

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562 WYLIE AND RUFINO

completing the diversion program, youth endorsing the disruptive
disorder cluster, �2(1) � 14.33, p � .001, were 1.5 times less
likely to be successfully discharged and youth endorsing the sub-
stance use cluster, �2(1) � 9.17, p � .01, were 1.44 times less
likely to be successfully discharged from the program. On the
other hand, youth with anxiety symptoms, �2(1) � 0.88, p � .35,
and mood disorders, �2(1) � 1.27, p � .26, were equally as likely
to complete the program successfully.

Table 2 presents mean ages and YLS/CMI risk scores for each
victimization type and mental health cluster. Overall, youth with
reported victimization were approximately the same age as youth
without reported victimization for sexual assault, F(1, 2789) �
0.52, p � .47, abuse/neglect, F(1, 2789) � 3.40, p � .07, and
person crime, F(1, 2789) � 2.58, p � .11. However older youth
were more likely to have experienced a reported property crime
incident as compared to those younger, F(1, 2789) � 10.767, p �
.001. For most types of victimization, youth had higher YLS/CMI
risk-level scores than youth without a reported instance of victim-
ization. More specifically, those reporting sexual assault, F(1,
2668) � 7.97, p � .01, abuse/neglect, F(1, 2668) � 14.07, p �
.001, and person crime, F(1, 2668) � 13.99, p � .001, scored
higher on the YLS/CMI than those without reported experiences.
There were no differences on YLS/CMI risk-level scores between
youth with a reported property crime victimization and youth
without a reported property crime victimization, F(1, 2668) �
0.01, p � .97.

Next, we conducted several bivariate comparisons to compare
juveniles who recidivated (using a binary yes/no variable) and
those who did not (see Table 3). Results demonstrated there were
significant gender differences, �2(1) � 28.81, p � .001, with
males 1.59 more likely to recidivate than females; and significant
racial/ethnic differences, �2(1) � 38.93, p � .001, with non-White
youth 1.52 more likely to recidivate than White youth. It should be
noted, however, that racial/ethnic differences are not necessarily
indicative of greater delinquency in non-White youth, but could
stem from system-wide issues of disproportionate minority con-
tact/racial ethnic disparities. Those who were unsuccessfully dis-

charged were 1.91 times more likely to recidivate than those who
were successfully discharged, �2(1) � 38.93, p � .001. For the
victimization types, results revealed significant differences in re-
cidivism rates for abuse/neglect, �2(1) � 7.97, p � .01, person
crime victimization, �2(1) � 16.16, p � .001, and property crime
victimization, �2(1) � 7.29, p � .01, showing that victims of
abuse/neglect were 1.54 times, victims of person crimes were 1.68
times, and victims of property crimes were 1.90 times more likely
to recidivate. There was, however, no significant difference in
recidivism for participants who were the victims of sexual assault,
�2(1) � 0.21, p � .65. With respect to mental health variables,
there were not significant differences in recidivism for juveniles
who endorsed anxiety symptoms, �2(1) � 3.69, p � .06, or
disruptive behavior symptoms, �2(1) � 0.59, p � .44. On the other
hand, there were significant differences for both mood symptoms,
�2(1) � 4.15, p � .05, and substance use symptoms, �2(1) � 4.18,
p � .05. More specifically, while juveniles endorsing the sub-
stance use cluster were 1.22 times more likely to recidivate,
juveniles endorsing the mood cluster were 1.25 times less likely to
recidivate.

In comparing the age at intake to diversion of those who
recidivated (M � 15.10, SD � 1.49) to those who did not (M �
15.08, SD � 1.70), there were no age differences between the two
groups, F(1, 2789) � .16, p � .69. Moreover, there were signif-
icant differences by risk level measured with the YLS/CMI total
score, F(1, 2668) � 81.73, p � .001, on recidivism, as those who
recidivated scored higher on the YLS/CMI (M � 8.56, SD � 4.42)
than youth who did not recidivate (M � 6.98, SD � 4.02).

Modeling Variables on Time to Recidivism

Last, we conducted a survival/hazard analysis with a stepwise
Cox regression to examine if time to recidivism (i.e., days to
failure) differed based on a history of reported victimization and
mental health symptom clusters. A hazard ratio (HR) greater than
1 indicates a shorter time to failure. In other words, juveniles with
that characteristic or risk factor recidivated more quickly than

Table 1
Gender, Race, and Discharge Type by Victimization Type and Mental Health Clusters
(in Percentages)

Variable

Gender Race Discharge

Male Female White Non-White Successful Unsuccessful

Victimization
Sexual assault .5%a 3.6%b 1.4% 2.0% 1.7% 1.9%
Abuse/neglect 6.7% 7.1% 4.8%a 8.8%b 6.1%a 10.8%b

Person crime 10.0% 9.7% 7.7%a 12.0%b 8.8%a 15.4%b

Property crime 2.8% 2.2% 3.3%a 1.9%b 2.3% 3.9%
Mental health

Anxiety 25.4%a 49.9%b 30.8%a 39.6%b 34.9% 37.3%
Mood 11.5%a 31.6%b 19.2% 20.0% 19.2% 21.6%
Disruptive 37.3%a 46.7%b 35.7%a 46.2%b 39.5%a 49.4%b

Substance use 20.6% 23.5% 29.5%a 14.4%b 20.7%a 27.3%b

Note. Percentages are presented as the presence of victimization and mental health within each level of gender,
race, and discharge type. Significantly different bivariate comparisons among gender, race, and discharge type
are represented with different superscripts. For example, for gender (e.g., male vs. female), the superscripts
indicate that there was a significant difference for sexual assault, anxiety, mood, and disruptive. All differences
were significant at p � .05.

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563VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

juveniles in the comparison group, after controlling for the other
variables in the model (Chung, Schmidt, & Witte, 1991; Wolff et
al., 2015). Several iterative regression models were estimated to
evaluate the variables of interest on recidivism. The results of this
analysis are presented in Table 4, and the HR function presented
by each type of victimization is presented in Figure 1.

The first model with only the youth characteristics was signif-
icant, �2(3) � 40.35, p � .001; race/ethnicity and gender signif-
icantly predicted recidivism, but age was not a significant predic-
tor. Specifically, juveniles who were non-White and male
recidivated sooner than juveniles who were White and female. In
Model 2, we added discharge type and risk level measured with the
modified YLS/CMI total score (i.e., without the Substance Use
subscale); the model was significant, �2(5) � 117.11, p � .001.
More specifically, juveniles with higher risk scores and who were
unsuccessfully discharged recidivated sooner than juveniles with
lower risk scores and those who successfully completed the pro-
gram. Gender and race/ethnicity remained significant, and age
became a significant predictor in Model 2.

Next, the four victimization variables were added to Model 3,
�2(9) � 143.08, p � .001, and it remained significant. Results
revealed that victims with reported incidents of abuse/neglect,
person crimes, and property crimes were more likely to recidivate

at a faster rate, but there was not a significant difference for
victims of reported sexual assault. The pattern for youth charac-
teristics were similar to the previous model. The final model,
which added the four mental health clusters, significantly pre-
dicted time to recidivism, �2(13) � 153.89, p � .001, though
the substance use cluster was the only mental health variable to
predict time to recidivism. This full model revealed that juveniles
who recidivated sooner were more likely to be male (HR � .75; CI
[.64, .88]), non-White (HR � 1.21; CI [1.04, 1.41]), unsuccessfully
discharged (HR � 1.31; CI [1.09, 1.57]), have a higher modified
YLS/CMI risk-level score (HR � 1.06; CI [1.04, 1.09]), have a
reported incident of abuse/neglect (HR � 1.37; CI [1.07, 1.75]),
person crime victimization (HR � 1.41; CI [1.14, 1.74]), and property
crime victimization (HR � 1.52; CI [1.06, 2.19]), as well as substance
use symptoms (HR � 1.12; CI [1.02, 1.43]). As the HRs indicate, the
three significant victimization variables were the strongest predictors
of time to recidivism, relative to the other significant variables of
race/ethnicity, gender, modified risk score, and discharge reason.

Table 2
Age and YLS/CMI Risk Level for the Presence and Absence of
Victimization Type and Mental Health Clusters

Variable

Age YLS

M (SD) M (SD)

Victimization
Sexual assault

Present 14.92 (1.29) 9.26 (4.53)a

Absent 15.09 (1.64) 7.43 (4.20)b

Abuse/neglect
Present 14.88 (1.68) 8.59 (4.17)a

Absent 15.10 (1.63) 7.38 (4.20)b

Person crime
Present 14.93 (1.70) 8.40 (4.67)a

Absent 15.10 (1.63) 7.36 (4.14)b

Property crime
Present 15.71 (1.42)a 7.48 (3.39)
Absent 15.07 (1.64)b 7.46 (4.23)

Mental health
Anxiety

Present 14.78 (1.65)a 8.20 (4.36)a

Absent 15.31 (1.51)b 7.10 (4.07)b

Mood
Present 14.92 (1.56)a 8.69 (4.28)a

Absent 15.17 (1.59)b 7.19 (4.14)b

Disruptive
Present 14.68 (1.60)a 8.89 (4.49)a

Absent 15.43 (1.49)b 6.51 (3.71)b

Substance use
Present 15.64 (1.24)a 8.93 (4.74)a

Absent 14.98 (1.64)b 7.07 (3.95)b

Note. Significantly different bivariate comparisons between the presence
and absence of victimization and mental health are represented with
different superscripts. For example, for property crime, the superscripts
indicate that there was a significant difference in age for those with and
without a reported incident of property crime (e.g., present vs. absent).
YLS/CMI � Youth Level of Service/Case Management Inventory. All
differences were significant at p � .05.

Table 3
Bivariate Comparisons of Demographics, Victimization Types,
and Mental Health Clusters on Recidivism (in Percentages)

Variable Recidivated

Gender
Male 33.9%a

Female 24.4%b

Race/ethnicity
White 25.6%a

Non-White 34.4%b

Discharge
Successful discharge 27.6%a

Unsuccessful discharge 42.2%b

Victimization type
Sexual assault

Presence 27.1%
Absence 30.1%

Abuse/neglect
Presence 39.1%a

Absence 29.4%b

Person crime
Presence 40.6%a

Absence 28.9%b

Property crime
Presence 44.4%a

Absence 29.7%b

Mental health
Anxiety

Presence 28.0%
Absence 31.6%

Mood
Presence 26.6%a

Absence 31.2%b

Disruptive
Presence 31.2%
Absence 29.8%

Substance use
Presence 33.8%a

Absence 29.4%b

Note. Significantly different bivariate comparisons within each variable
represented with different superscripts. For example, for gender (e.g., male
vs. female), the superscripts indicate that there was a significant difference
for gender on recidivism. All differences were significant at p � .05.

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Discussion

The juvenile justice system is built on the notion that young
people are malleable and have a likelihood of rehabilitation, which
may be especially true for lower risk youth who come into contact
with law enforcement for less serious offenses. Assessing the
extent to which specific risk factors are associated with continued
reoffending after initial juvenile justice involvement, is important
to consider in the development of theoretical frameworks and the
implementation of evidence-based practices aimed at reducing
future system involvement. We investigated the impact of two risk
factors, prior victimization and mental health symptoms, using
official law enforcement records for both victimization and recid-
ivism.

Approximately one in six of the juveniles in this sample was the
victim of a reported crime, either abuse/neglect, sexual assault, or
the victim of property or person crime offenses. Although this is
probably an underestimation, as studies that use self-report meth-
ods have found higher rates of self-reported victimization (e.g.,
Abram et al., 2004), there may be something unique about reported
incidents as compared to those that go unreported—a pattern that
likely differs by victimization type. For instance, one explanation
may be that reported incidents of property or person crimes could

be more serious because they came to the attention of law enforce-
ment, as opposed to less serious incidents of these types of crimes.
On the other hand, it may be that more serious incidents of child
maltreatment are less likely to be reported (Brown, Cohen, John-
son, & Salzinger, 1998; Swahn et al., 2006). Reported incidents
could, alternatively, be the result of youth or familial differences.
For example, Swahn and colleagues (2006) compared self-reported
child maltreatment to official court records in a sample of youth in
detention and found that of those who self-reported maltreatment,
females were more likely to have an official court record than
males, and African Americans were more likely to have an official
court record than Whites.

Overall, the most frequent reported type of victimization was
person crimes, followed by abuse/neglect and property offenses,
with fewer reported incidents of sexual assault. We attempted to
compare the rates of victimization in this sample to other studies,
however because victimization has been operationalized in multi-
ple ways, comparisons were not meaningful. Future research
should compare whether early system-involved youth experience
similar rates of victimization using official records and varying
types of victimization to adjudicated and/or detained youth. In
examining gender differences, the only type of victimization that
differed by gender was sexual assault, in which females were more
likely to experience sexual assault than males. Results from pre-
vious research on gender differences in victimization experiences
have been mixed. For instance, although Abram and colleagues
(2004) found that males were more likely to report a traumatic
event (defined broadly by witnessing or experiencing violence)
than females, Conrad and colleagues (2014) found no differences
by gender for a history of child sexual abuse. These data also
suggest there may be some differences based on race/ethnicity for
reported incidents of victimization. Non-White youth were signif-
icantly more likely to have reported incidents of both abuse/
neglect and person crimes, but White youth were more likely to
have reported incidents for property offenses. Because our victim-
ization data are limited by official records, it is not clear whether
non-White youth actually have higher rates of victimization, or
whether these youths are just more likely to have system involve-
ment following victimization.

In both the bivariate comparisons and multivariate model ex-
amining time to recidivism, abuse/neglect and person crime vic-
timization were related to both discharge from diversion and
recidivism (property offense victimization was only related to
recidivism). It is unclear from this data whether victimization in
these types of incidents directly relates to difficulties in completing
diversion or future reoffending, or whether these rates are an
artifact of exposure to system involvement because of victimiza-
tion. Moreover, the relationship between being the victim of a
person or property crime and recidivism, could be the product of
delinquent peers or engaging in delinquent lifestyles. Within this
literature, scholars have proposed several avenues for this connec-
tion. The first is related to Routine Activities Theory, which
suggests that engaging in criminal behavior puts individuals at
higher risk of victimization because they find themselves in situ-
ations where they may be more likely to be victimized (Cohen &
Felson, 1979). Others have proposed that exposure to victimization
serves as a learned experience within certain subcultural environ-
ments (Fagan, Piper, & Cheng, 1987), whereas general strain
theory (Agnew, 1992) asserts that the negative experiences of

Table 4
Results From a Hazard Cox Regression Predicting Recidivism

Model Variable Exp(B) 95% CI

1 Age 1.04 .99–1.09
Race 1.40��� 1.20–1.61
Gender .71��� .61–.82

2 Age 1.07� 1.02–1.12
Race 1.20� 1.04–1.40
Gender .70��� .61–.82
Modified risk score 1.06��� 1.04–1.08
Discharge 1.37�� 1.14–1.63

3 Age 1.06� 1.01–1.12
Race 1.18� 1.02–1.37
Gender .71��� .61–.82
Modified risk score 1.06��� 1.04–1.08
Discharge 1.33�� 1.11–1.59
Sexual assault .84 .47–1.49
Abuse/neglect 1.37� 1.07–1.75
Person crime 1.40�� 1.14–1.73
Property crime 1.53� 1.07–2.20

4 Age 1.05 .99–1.10
Race 1.21� 1.04–1.41
Gender .75��� .64–.88
Modified risk score 1.06��� 1.04–1.09
Discharge 1.31�� 1.09–1.57
Sexual assault .84 .47–1.50
Abuse/neglect 1.37� 1.07–1.75
Person crime 1.41�� 1.14–1.74
Property crime 1.52� 1.06–2.19
Anxiety cluster .92 .77–1.10
Mood cluster .84 .68–1.03
Disruptive cluster 1.00 .85–1.17
Substance use cluster 1.12� 1.02–1.43

Note. Gender: male � 0; race: White � 0; victimization: nonvictim � 0;
mental health clusters: 0 � not endorsed. The modified risk score included
the YLS-total score with the substance use subscale values removed from
the total score to reduce multicollinearity with the substance use mental
health cluster. Exp(B) � exponentiation of the B coefficient.
� p � .05. �� p � .01. ��� p � .001.

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565VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

victimization create strain, which contribute to delinquency (Ma-
nasse & Ganem, 2009).

Our results demonstrate that two thirds of these early system-
involved juveniles experience one or more mental health symp-
toms, with one in five reporting substance use or mood symp-
toms, one in three reporting anxiety symptoms, and almost half
reporting disruptive disorder symptoms. In comparing these
proportions to research using the same mental health assess-
ment tool involving juveniles referred to probation (McReyn-
olds et al., 2007, 2010) and incarcerated juveniles (Wasserman
et al., 2002), it appears that early system-involved juveniles
may experience similar mental health symptoms as deeper end
justice-involved juveniles. In examining the symptom clusters
specifically, a higher proportion of our sample endorsed anxi-

ety, mood, and disruptive symptoms, but fewer endorsed sub-
stance use symptoms when compared to McReynolds et al.
(2010) and Wasserman et al. (2002). Even though this sample
differed from these previous studies in specific proportions,
across all studies, disruptive disorders are most common among
justice-involved youth and mood disorders are least common
(Shufelt & Cocozza, 2006). As such, there may be few differ-
ences between early justice-involved juveniles and later justice-
involved juveniles in terms of how mental health symptoms are
presented.

Furthermore, the results of the present study show the impact of
victimization and mental health on early justice-involved youth. In
support of the first hypothesis, juveniles with a history of victim-
ization recidivated sooner than juveniles without a history of

Figure 1. Hazard function for each victimization type.

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566 WYLIE AND RUFINO

victimization (Cottle et al., 2001; Vidal et al., 2017). Although
previous literature on mental health and recidivism in juvenile
justice is mixed, the bivariate analyses and multivariate analyses
demonstrated that participants endorsing substance use were more
likely to recidivate, which is supported by previous research
(Wolff et al., 2015; Wierson & Forehand, 1995). The bivariate
comparisons also found that those who endorsed the mood disor-
der symptoms were less likely to recidivate, which differs from
studies that have not found a significant relationship between
mood disorders and recidivism (McReynolds et al., 2010). One
possibility is the mood cluster acts as a protective factor against
recidivism, as juveniles who are feeling depressed are less likely to
engage in social activities with peers, where they would be more
likely to encounter peer pressure or engage in risky shift behaviors.
Furthermore, juveniles who are feeling depressed may be experi-
encing anhedonia and a lack of energy.

As partially hypothesized, however, once the mental health
clusters were included in the multivariate model with demographic
variables, the modified risk level score, and victimization, only the
substance use mental health cluster predicted time to recidivism,
while the mood mental health clusters did not. Although substance
use remained a significant predictor of time to recidivism even
while controlling for criminogenic risk factors, youth in this sam-
ple with mental health issues did exhibit higher criminogenic risk
and needs profiles as measured with the YLS/CMI compared with
those without mental health issues as previous research has indi-
cated (Guebert & Olver, 2014; Schubert et al., 2011). One caveat,
however, is that the risk/needs profile may not be independent of
mental health symptoms because there is overlap between items on
the YLS/CMI and mental health issues. Although we were able to
partially address this by removing the Substance Use subscale
from the YLS/CMI and creating a modified YLS/CMI risk score
without substance use, there are additional items on the YLS/CMI
that have overlap with mental health symptoms that we were not
able to separate. For instance, the Personality/Behavior YLS/CMI
subscale measures short attention span, poor frustration tolerance,
and tantrums, which may overlap with symptoms of disruptive
disorders. The Leisure/Recreation YLS/CMI subscale measures
personal interests, which could be affected by depression symp-
toms. Future research should continue to explore these domains
and examine measurement tools that can more accurately parse out
these risk factors for delinquency.

The results of the current study demonstrate the importance of
addressing mental health concerns in juvenile diversion programs,
which are one of the first points that youth may touch in the
juvenile justice system. While research guided by the Risk-Needs-
Responsivity model has generally found that only treating mental
health issues is mostly ineffective (e.g., Andrews & Bonta, 2010),
research has demonstrated that addressing mental health issues and
criminogenic needs can be effective (Ashford, Wong, & Stern-
bach, 2008) because it adheres to the responsivity principle. The
data from this study demonstrate there is a relationship between
mental health needs and discharge reason—namely, that youth
endorsing substance use and disruptive disorders were less likely
to successfully complete the diversion program. Perhaps by spe-
cifically attending to these mental health needs, diversion pro-
grams can adapt programming for youth with these issues, which
may contribute to more positive outcomes such as successful

completion of the program and reduced recidivism (Andrews &
Bonta, 2010).

Limitations and Future Directions

This study has several limitations that should be considered
when interpreting the results. The present sample was comprised
entirely of early system-involved juvenile offenders who were
deemed eligible for diversion and who chose to be in diversion
rather than go through traditional juvenile court. As such, the
sample may be limited to those with eligible offenses (minor
offenses or status offenses) but would not include more serious
first-time offenders. There may also be differences between the
types of youth who choose diversion and those who choose tradi-
tional juvenile court, including youth in more need of services
(who may not otherwise have access to services) or youth whose
families choose traditional court because it is often less time
consuming than committing to a juvenile diversion plan. Although
this sample was diverse, with approximately half identifying as
White, one third as Black, and about 14% as Hispanic, this
diversity does not necessarily represent the population of the city
from which the data were collected, with Black youths overrepre-
sented in this sample. This overrepresentation may be due to any
number of related variables, including disproportionate minority
contact that could impact the variables of interest and recidivism
outcomes. Although the sample did include a small number of
other races and ethnicities (e.g., Asian, Native Americans), these
groups were too small to make meaningful comparisons. Future
research may consider a more ethnically diverse sample.

Although previous research has consistently found various types
of victimization do predict recidivism, better understanding how
the types of victimization differ by sample characteristics and how
different types of victimization impact recidivism warrants further
investigation. The victimization variable was limited by using
official law enforcement reports, which was likely an underesti-
mation of victimization incidents in general. Official reports of
victimization may also result in an underestimation of certain types
of victimization that go unreported to law enforcement (e.g., child
abuse/neglect, mutual assault). Future research may consider uti-
lizing a range of victimization measures, including self-report data
and official law enforcement records. Finally, although we know
that victimization and mental health symptoms are related to
recidivism, at least in bivariate comparisons, our data does not
explain why these risk factors contribute to recidivism. Research-
ers may consider specifically testing theoretical frameworks aimed
at measuring for the underlying relationships between victimiza-
tion, mental illness, and reoffense.

Conclusion

Juvenile diversion programs embrace the mission of the juvenile
justice system, namely that youth can be rehabilitated by linking
them to services and that juveniles who commit less serious
offenses should not be formally processed through “the system.”
This study provides preliminary evidence for some of the risk
factors of juveniles most likely to recidivate after being connected
to those services and diverted from the system. The three victim-
ization type variables were the strongest predictors of recidivism,
even while controlling for risk level and other juvenile character-

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567VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

istics. As such, programs should specifically focus on trauma-
informed programming that addresses the form of victimization a
youth has experienced. Moreover, there is a need for early inter-
vention tailored to the needs of abused, neglected, and victimized
youths before they interact with the justice system or when they
first enter the juvenile justice system. Early interventions should
provide victimized youth with resources to increase resilience and
teach positive and proactive coping strategies to minimize the
effects that victimization may have on mental health functioning,
and subsequent justice involvement.

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Received February 9, 2018
Revision received September 24, 2018

Accepted September 26, 2018 �

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569VICTIMIZATION AND MENTAL HEALTH ON RECIDIVISM

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http://dx.doi.org/10.1111/j.1745-9125.1993.tb01127.x

• The Impact of Victimization and Mental Health Symptoms on Recidivism for Early System-Involved J …

Operationalizing Victimization
Victimization and Mental Health Symptoms
Victimization and Delinquency
Mental Health Symptoms
The Current Study
Method
Participants
Study Design and Procedure
Measures
Demographics
Successful completion of diversion
Risk-level
Official records of victimization
Mental health symptoms
Recidivism
Data Analysis and Hypotheses
Results
Bivariate Relationships
Modeling Variables on Time to Recidivism
Discussion
Limitations and Future Directions
Conclusion
References

Research Review: Independent living programmes: the
influence on youth ageing out of care (YAO)

Anna Yelick
Lecturer, College of Social Work, Florida State University, Tallahassee, FL, USA

Correspondence:
Anna Yelick,
College of Social Work,
Florida State University,
296 Champions Way,
University Center Building C,
Tallahassee, FL 32306,
USA
E-mail: amy12@my.fsu.edu

Keywords: educational attainment,
employment, independent living
programmes, life skills, productive
outcomes, youth ageing out of care

Accepted for publication: December
2014

A B S T R AC T

Independent living programmes (ILPs) aid in promoting productive
outcomes for youth ageing out of care (YAO). This narrative review
aimed to determine if sufficient evidence exists to substantiate state-
ments regarding the effectiveness of ILPs based upon outcome studies
published from January 2006 through December 2012. Are current
ILPs effectively promoting independent living and productive out-
comes among youth leaving foster care, relative to similar youth who
do not participate in an ILP? Six studies published in English, in the
USA and in peer-reviewed journals included non-experimental design
(n = 1), quantitative designs (n = 2), mixed methods design (n = 2) and
randomized design (n = 1). Five outcomes addressing education,
employment, housing, mental health, and living skills emerged. Weak
evidence that ILPs effectively aid YAO exists. Additionally, inconsist-
encies exist in methodology. Finally, differences in important compo-
nents in the ILPs exist, making comparisons difficult.

I N T R O D U C T I O N

Approximately 10% (23 396) of youth emancipated
from the care system in 2012 (Adoption and Foster
Care Analysis and Reporting System (AFCARS)
2011). These emancipated youth (youth who have
aged out of the system, usually when they reach 18
years old) typically face disadvantages in terms of
educational attainment and employment outcomes
compared to non-fostered youth (Unrau et al. 2011).
For example, 50% of foster youth obtain a high school
diploma or general educational development (GED)
degree compared to nearly 70% of non-fostered youth
(Sheehy et al. 2001; Wolanin 2005; Unrau et al.
2011). Post-secondary education is not encouraged
among the foster youth population as approximately
15% of foster youth enroll in college-preparatory
classes compared to 32% of non-fostered youth
(Unrau et al. 2011). Further, while nearly 79% of
foster youth express an interest in attending a post-
secondary education programme (Courtney et al.
2010), as few as 7–13% enroll in post-secondary edu-
cation programmes (Casey Family Programs 2010)
and fewer than 6% obtain post-secondary degrees
(Pecora et al. 2010).

Youth ageing out of care (YAO) also face challenges
in terms of housing and life skills. YAO are independ-
ent, and as such, must find housing, pay bills and find
employment shortly after leaving care.YAO often lack
social and familial support, which arguably leads to a
lack in life skills and resources to successful independ-
ent living (Lemon et al. 2005; Montgomery et al.
2006; Collins et al. 2008; Avery & Freundlich 2009;
Avery 2010; Harder et al. 2011). Additionally, YAO
tend to live independently at an earlier age compared
to non-fostered youth – many of whom return home
or have financial and emotional support well into their
20s (Lemon et al. 2005; Montgomery et al. 2006).

Independent living programmes (ILPs) were estab-
lished to aid YAO obtain productive positive out-
comes, such as educational attainment, employment
stability, housing and life skills (Montgomery et al.
2006; Petr 2006; Naccarato & DeLorenzo 2008;
Uzoebo et al. 2008; Mares 2010; Kroner & Mares
2011; Mares & Kroner 2011; Powers et al. 2012).
The Independent Living Initiative established ILPs,
which was an amendment to the Social Security Act
(Mares & Kroner 2011), and established aid for foster
youth to live independently, as well as enabled states
to develop life skills, academic achievement and

bs_bs_banner

doi:10.1111/cfs.12208

© 201 John Wiley & Sons LtdChild and Family Social Work 2017, 22, pp 515–526515 5

mailto:amy12@my.fsu.edu

vocational training programmes to circumvent home-
lessness, dependence on public assistance and institu-
tionalization after emancipation (Hardin 1987). ILPs
were further develop with the enactment of the John
Chafee Foster Care Independence Program of 1999
and the Foster Care Independence Act of 1999, which
provided states with additional funding in response to
independent living research findings and child welfare
advocates calling for an amendment of the Social
Security Act (Allen & Bissell 2004). The Fostering
Connections to Success and Increasing Adoptions Act
of 2008 improved outcomes for children in foster care
by expanding the definition of child. This definition
included youth ages 18–20, enrolled in secondary
education, post-secondary education or vocational
training programmes; employed 80 hours a month or
more; or who are incapable of attending school or
work because of a medical condition (Mares & Kroner
2011).

Approximately two-thirds of eligible youth receive
independent living services (Courtney 2005; Avery
2010), indicating that ILPs are widely used by youth
exiting the foster care system. ILPs aim to promote
skills for independent living (Reilly 2003; Lemon et al.
2005; Montgomery et al. 2006; Geenen & Powers
2007), encouraging youth to become productive
members of society and attain positive productive out-
comes (independent living, education, employment
and increased life skills) despite lacks in social and
familial support (Montgomery et al. 2006; Mares &
Kroner 2011).

Effectiveness of ILPs

Two systematic reviews have been completed
(Montgomery et al. 2006; Naccarato & DeLorenzo
2008) assessing the efficacy of ILPs in accomplishing
the projected aims discussed earlier.The Montgomery
et al. (2006) review aimed to examine whether ILPs are
effective at providing youth with skills that enhance
their transition to independence.They included studies
conducted prior to January 2006, which examined
educational attainment, employment, housing, health
and life skills for youth leaving the care system. They
excluded any study that examined programmes specifi-
cally designed for special populations (i.e. special
needs, teen parenting, juvenile justice concerns).

The authors suggest evidence indicating that ILPs
improved educational, employment and housing out-
comes for YAO; however, the evidence was weakened
by evaluation methodology – specifically, a lack of
randomized control trials (RCTs), as non-randomized

studies are susceptible to bias. Confidence regarding
the effectiveness of the ILP was low because of the
inability to say with certainty that observed differences
are attributable solely to the ILP. Despite this limita-
tion, approximately 55% of the ILP group graduated
from high school. However, discrepancies between
YAO and the general population youth still exist.
Approximately 86% of the general population youth
graduated from high school, according to the US
National Center for Education Statistics (Greene
2002). In addition, the national rate for employment
differs between YAO and the general population
youth, indicating that ILPs have yet to bridge the gap
in positive productive outcomes for YAO compared to
the general population youth.

The second systematic review by Naccarato &
DeLorenzo (2008) aimed to examine studies regard-
ing the effectiveness of ILPs in youth transitioning out
of the care system from 1990 until 2006 in and
outside the USA. However, the review only included
studies in the USA and UK. The authors reviewed 19
articles, which met the four criteria: (i) the ILP aimed
to increase readiness for youth leaving the care system;
(ii) reported on education, employment, housing and
mental health; (iii) published in a peer-reviewed
journal and in English; and (iv) discussed transitional
services.

The authors suggested that the studies they
reviewed offered recommendations regarding improv-
ing services to YAO. Some of the recommendations
were to improve ILP practice, policy and research.
The authors also suggested that the studies varied
greatly in measurement of the ILP, specifically, in
sample size, demographics, placement histories,
support networks and outcome measures.The authors
recommended a national database with input from
researchers and practitioners in the field in order to
design a functional information system. Non-uniform
measurements make it difficult to determine the effec-
tiveness of ILPs, indicating service goals and quality
often vary among different ILPs (Courtney 2005;
Avery 2010), making it difficult to formulate general
assessments of ILPs and the effectiveness in aiding
YAO.

Purpose of paper

This paper aims to examine newer literature published
from January 2006 through December 2012 to deter-
mine if sufficient evidence exists to substantiate state-
ments made that ILPs effectively promote productive
outcomes (i.e. educational attainment, employment,

Research Review: Independent living programmes A Yelick

© 201 John Wiley & Sons LtdChild and Family Social Work 2017, 22, pp 515–52651 56

housing, mental health and life skills) for youth
leaving the care system. The paper includes only
studies conducted and published within the USA, in
English and in peer-reviewed journals.

Research question

Are current ILPs effectively promoting independent
living and productive outcomes among youth ageing
out of the care system relative to similar youth who do
not participate in an ILP?

M E T H O D S

Study selection

The present paper reviewed peer-reviewed studies
published in the USA and in English. The review
includes quasi-experimental and non-experimental
group outcome studies. The review included several
additional inclusionary and exclusionary criteria. The
inclusionary criteria were as follows: (i) the study
must contain information regarding an ILP; (ii) the
study must have measureable outcomes (educational
attainment, employment, housing, mental health/
special needs or life skills); and (iii) the study must
examine ILPs for foster youth or residential care
youth, or youth ageing out of the care system (YAO)
only. The exclusionary criteria were (i) if the study did
not examine ILPs; (ii) if the study was published prior
to January 2006; or (iii) if the study was conducted
outside the USA.

The electronic databases searched included the
Cochrane Central Register of Controlled Trials, The
Campbell Library, PsycINFO, Sociological Abstracts,

Applied Social Sciences Index and Abstracts (ASSIA)
and Web of Science. The keyword search terms were
(i) ab(Foster Care Youth) AND ab(Independent
Living); (ii) ab(Foster Youth) AND ab(Independent
Living Programs); (iii) ab(Independent Living) AND
ab(Evaluation) AND ab(Randomized Control Trials);
and (iv) ab(Foster Care) AND ab(Foster Care Youth)
AND ab([Independent Living Programs OR ILPs])
AND ab(Outcomes).This resulted in an initial pool of
135 citations. Of these, 32 abstracts were examined,
resulting in the inclusion of six primary studies (refer
to Table 1 and Fig. 1).

Search results

The six primary studies utilized qualitative methods
(one study), mix methods (two studies) and quantita-
tive methods (three studies) to assess the effectiveness
of ILPs from across the USA (Table 2 provides a
summary of each study). The study participants were
foster youth aged 16 and older preparing to leave the
care system, or in the case of the qualitative study – the
participants could include service providers. Out-
comes of interest include secondary education,
post-secondary education, employment, housing
attainment, mental health or other special needs, and
achieving life skills.

The study by Petr (2006) utilized a qualitative
approach to evaluate the Kansas Independent Living
Program in addition to five private contract agencies
to assess youths’ perspectives (n = 27) regarding the
quantity and quality of independent living services.
This study utilized a convenience sample and
included two groups: youth still in custody (n = 19)
and youth out of custody (n = 8). Only youth aged 16
and up were included in the study (mean age of 17.3).

Table 1 Database search

Databases search

Database Date Results

Cochrane Library Since 2000 0
Campbell Library Since 2000 1 – Systematic review completed in 2006 –

restricted review to after 2006
Web of Science January 2006 through December 2012 51
PsycINFO January 2006 through December 2012 78 – These four databases were searched

simultaneously in order to reduce duplications.
Another review was discovered; however, it also
examined ILPs prior to January 2006

Sociological Abstracts January 2006 through December 2012
Applied Social Science Index

and Abstracts (ASSIA)
January 2006 through December 2012

Social Services Abstracts January 2006 through December 2012

ILP, independent living programmes.

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Fifty-one per cent (n = 14) of the total sample were
male (57% for in custody, 37.5% for out of custody).
Approximately 51% (n = 14) of the total sample were
Caucasian (42% for in custody and 87.5% for out of
custody), while 33% (n = 9) of the total sample were
African-American (47% for in custody). Of the eight
participants in the out of custody group, six had a high
school diploma (or GED equivalent) and two were in
college. There were five outcomes assessed in this
study: (i) education; (ii) mentors and support systems;
(iii) life skills training; (iv) vocational preparation; and
(v) knowledge of post-custody independent living
benefits.The interviews were transcribed and analysed
using Atlas ti, a qualitative software program. Petr
coded units of the interview text according to the
themes presented and then grouped the themes by
commonality.

The study by Uzoebo et al. (2008) examined a spe-
cific ILP, VISIONS, utilizing a mixed methods
approach. It has been included in the review based
upon the important perceptions discussed by youth
receiving services. The quantitative data were col-
lected using the Ansell Casey Life Skills Assessment
(ACLSA). There were both pre-test and post-test
assessments of the ACLSA for 89 participants. Quali-
tative data were gathered using the Life Skills Evalu-
ation Questionnaire for 24 participants. The average
age of the participants was 16 years, with 63% female
and 61% African-American. The average length of
stay in the programme was 17 months. The outcomes
of this study included determining perceptions of the

life skills received by the participants regarding the
benefits of the programme, barriers to skills acquisi-
tion and the role of the youth–mentor relationship in
promoting skills development.

The study by Mares (2010) utilized a mixed
methods approach using a focus group (n = 35) as well
as administrative data from Lucas County in Ohio.
The study included a sample of 108 youth who had
emancipated from an ILP from 2005 through 2007.
The information collected via the tracking data
included demographic characteristics, clinical charac-
teristics, foster home placements, outcomes at dis-
charge and receipt of post-emancipation services. Five
needs emerged during the focus groups: (i) higher
amount for clothing vouchers; (ii) assistance obtaining
a driver’s licence; (iii) provide home-based independ-
ent living life skills training; (iv) ensure confidentiality
of foster care placement packet; and (v) address the
perception of unfair/unequal treatment by the foster
parent(s) towards the foster youth. These themes
included the expressed views of the participants and
observations made by the research team. The modera-
tor, a social work student, and the author discussed
the observations during meetings. The transcriptions
provided illustrative quotations for each theme iden-
tified. In addition to the qualitative reports from the
youth participants, surveys were collected from 83
public and private service providers using an online
survey constructed by the author with input from the
research team. The survey included 22 items contain-
ing respondent information, programme information,

Potentially relevant studies
identified and screened for
retrieval (n=135)

Ineligible studies excluded based
on title, language or date. In
addition, studies using “grey”
literature have also been excluded
(n=103)

Abstracts of studies retrieved
(n=32)

Studies excluded if not looking at
independent living programmes/
transitions for foster youth (n=21)

Potentially appropriate studies for
review; studies evaluated using the
inclusion/ exclusion criteria
worksheet (n=11)

Studies excluded from the review
if there was no measurable
outcome related to an independent
living programme (n=5)

Primary studies with usable
information by outcome (n=6)

Figure 1 Flowchart of the primary
six studies included in this review.

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Table 2 Summary of the six primary studies included in the narrative review

Study Year Title Study population
Study
design Primary outcome

Kroner & Mares 2011 Living arrangements and
level of care among
clients discharged from a
scattered-site
housing-based
independent living
programme

Foster and former foster
youth mean age 17.86

XO
YO
OXO

Housing outcomes: Three-fourths of youth
were discharged to an independent level
of care, 28% living by self, 13% living with
friend, or 17% living with a relative. 55% of
sample attained a status of independent
living.

Mares & Kroner 2011 Lighthouse independent
living programme:
Predictors of client
outcomes at discharge.

Foster and former foster
youth mean age 17.9

OXO
OYO

Educational outcome: They indicated that
participants with mental-health problems
were less likely to complete high school
(0.60). In addition, participants who stayed in
the programme longer were more likely to
complete high school (0.96). Participants who
were older at admission were also more
likely to complete high school (1.55 and 2.35).

Employment outcome: Being 1 year older when
entering into the programme predicated high
rates of employment (1.55 to 2.35). Staying at
least 1 month longer before exiting the
programme also predicted high rates of paid
employment (1.10). Participant without
mental-health problems were also more
likely to have paid employment (0.460).

Housing outcome: Participants who were older
when entering the programme were more
likely to live independently (1.55 and 2.35) at
discharge. Participants who remained in the
programme at least 1 month longer were also
more likely to have independent housing
(1.10). Participants who reported being
parents also more likely to have independent
housing at discharge (2.0).

Mental health/special needs outcome: Youth
with mental-health problems are less likely
to complete high school (0.61), find
employment (0.64) or establish independent
housing (0.68).

Uzoebo
et al.

2008 Deconstructing youth
transition to adulthood
services: Lessons learned
from the VISIONS
programme

Foster and former foster
youth mean age 16.0

OXO
OYO

Life skills outcome: Participants reported
higher mastery of skills in areas of daily living
skills, work life, money management and
budgeting, and self-care. At follow-up,
participants demonstrated an increase in
skills acquisition from 52% to 55%.
Participants indicated receiving training in a
class room setting was less efficacious to
learning via a mentor or from ‘real-life’
experiences.

Petr 2006 Foster care independent
living services: youth
perspectives

Foster and Former Foster
Youth mean age 17.3

– Educational outcome: 26% behind in
educational progress and goals, one
participant enrolled in GED programme.
Increased number of placements often
indicates a decrease in educational
attainment.

Employment outcome: 10 participants were
working at paid jobs in the community. Two
participants in the out-of-custody group were
working part-time and attending college.

Life skills outcome: 26% of the youth indicated
that they had not received any life skills
training. Two of the youth indicated it was
offered but they refused. 63% of the youth
who received the life skills training indicated
they were in one of three settings: a class
room setting, mental-health agency, or group
home facility. These youth also indicated
receiving life skills training from foster
parents in a less formal, day-to-day basis.

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Table 2 Continued

Study Year Title Study population
Study
design Primary outcome

Mares 2010 An assessment of
independent living needs
among emancipating
foster youth

Foster and former foster
youth

– Educational outcome: Secondary education
support was identified as one of the most
common services available. However,
financial support for college was among the
least common service available or offered
according to service providers.

Housing outcome: Housing assistance was
among the most helpful service identified for
youth aging out of care. However, affordable
housing and structured transitional housing
were identified as gaps for youth within ILPs.

Life skills outcome: Life skills training was
identified as being among the most helpful
for emancipating youth, however, hands-on
life skills training was identified as a gap in
service. This is also identified as one of the
greatest unmet needs within ILPs.

Powers et al. 2012 My life: Effects of a
longitudinal, randomized
study of
self-determination
enhancement on the
transition outcomes of
youth in foster care and
special education

Foster and former foster
youth mean age 16.8

OXO O
OYO O

Education outcome: 38% of the intervention
group and 28% of the comparison group
completed secondary education. At 1-year
follow-up, 72% for intervention and 50% for
comparison group completed secondary
education. Three youth were participating in
post-secondary education and 26% of youth
were participating in post-secondary
education at 1-year follow-up.

Employment outcome: 14% of the intervention
group and 19% of the comparison group
reported working paid jobs at baseline. At
post-intervention, 34% of the intervention
group and 16% of the comparison group
reported working paid jobs. At 1-year
follow-up, 45% of the intervention group and
28% of the comparison group had a paid job.

Housing outcome: At post-intervention, 63% of
participants were still in foster care, six
participants were adopted or reunited with
birth family, 14 participants were living with
friends or a partner in their own apartment,
one participant had housing provided
through Job Corps and two participants
identified as being homeless. At 1-year
follow-up, 57% of participants had exited
care, 15 participants reported being reunited
or adopted, 14 participants were living in
their own apartment, four participants were
residing in college dormitories, one
participant was in military housing and one
participant had housing through Job Corps.
60% of the comparison group reported
having a different placement from the year
before compared to 50% of the intervention
group, indicating a trend toward placement
stability.

Mental health/special needs outcome: 40% of
the sample had emotional/behavioural
problems, 10% of the sample had intellectual
disabilities, 16% had speech/language
problems, 26% had a learning disability, 5%
were considered to be on the autism
spectrum, and 26% developmental disabilities
services. The youth who received the
intervention fared better in high school
completion and fared better in employment
outcomes.

ILP, independent living programme; XO: indicates the research design included only an intervention and post-test (no pre-test); YO: indicates an
alternative design with only an intervention (or treatment as usual) and post-test (no pre-test); OYO: indicates an alternative design with a pre-test,
intervention (or treatment as usual), and post-test; OXO: indicates a research design with a pre-test, intervention, and post-test.

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and views on helpful and needed independent living
services.The response rate for the survey was 28% (23
respondents).

The study by Kroner & Mares (2011) sampled
youth who admitted into and discharged from the
Lighthouse ILP from 2001 through 2006. The initial
number of youth was 455; however, the final sample
size included 367 participants because of the missing
data. This experimental study compared two groups –
youth with discharge living arrangements and youth
without discharge living arrangements. The authors
found no statistically significant differences between
these two groups at the initial assessments.There were
22 different living arrangement categories divided into
four levels of care from lowest (described as living in
the most independent and stable housing) to the
highest (described as living in the least independent
and most unstable housing). The authors used inde-
pendent samples t-test and chi-square tests in order to
compare youth with living arrangement data to those
without such data available. In addition, the use of an
analysis of variance (ANOVA) compared characteris-
tics of the youth entering the programme from the
youth being discharged from the programme.

The study by Mares & Kroner (2011) sampled 385
youth admitted into the Lighthouse ILP during 2001
through 2005. The authors utilized an experimental
study comparing youth with mental-health concerns
to those without such concerns. The youth had an
average age of 17.9 years, with a range from 16 to 20.
The length of stay averaged 9.9 months with a range
from 0 to 32 months. Sixty-nine per cent of the
sample received some life skills training prior to dis-
charge, including employment skills training (54%),
vocational training (16%), GED preparation (8%)
and violence prevention (8%). Originally, the authors
examined 22 dichotomous risk factors; however, the
six domains pertinent to the study only included 19
risk factors. The six domains included mental health
and substance abuse, socialization, delinquency, teen
parenting, cognitive impairment, and motivation and
health. There were four dichotomous outcomes meas-
ured: completing high school or GED, being
employed or completing a vocational training pro-
gramme, living independently (i.e. renting an apart-
ment or home, either alone or with someone else), and
completing high school, being employed, and living
independently. An exploratory factor analysis identi-
fied clinical risk factor groups using the principal
component method of extraction.The authors utilized
logistic regression to examine the association between
clinical risk factors and client outcomes, controlling

for socio-demographic characteristics, length of stay
and life skills training.

Finally, the study by Powers et al. (2012) examined
an ILP specifically designed for foster youth who
received special education services. While this sub-
population differs from the general foster care popu-
lation, this study was included because it fell within
this review’s inclusionary criteria. This study
employed a randomized methodology, comparing
Foster Care Independent Living Program (FCILP)
and TAKE CHARGE. There were four criteria for
inclusion, youth must (i) receive special education
services; (ii) be between 16.5 and 17.5 years of age;
(iii) be under the guardianship of Oregon DHS; and
(iv) attend a large school district. The approximate
length of stay in the TAKE CHARGE group was 12
months; youth in the FCILP received services as
normal. There were 69 participants at baseline (33 in
the intervention group and 36 in the comparison
group), enrolled over three study waves and randomly
assigned to either the treatment or comparison group.
There were several outcomes measured at baseline,
post-intervention and 1-year follow-up; however, for
the purposes of this review, only three will be exam-
ined; high school completion, employment and living
status. At post-intervention, 60 participants were
assessed (29 intervention and 31 comparison), and at
1-year follow-up, 61 participants were assessed (29
intervention and 32 comparison). The attrition rate
was 13% at post-intervention and 11% at 1-year
follow-up. At baseline, the mean age for the partici-
pants was 16.8 years, with 41% of the sample identi-
fied as female. Approximately 40% of the participants
attended an alternative school and approximately 26%
of the participants received developmental disability
services.

A R E V I E W O F T H E S I X
P R I M A R Y S T U D I E S

Participants’ characteristics

The average age of the participants, calculated using
reported mean ages across the six primary studies, was
17.1 years.The studies indicated that youth often have
mental-health problems (64% for Kroner & Mares
(2011); 47% for Mares & Kroner (2011); and 34% for
Mares (2010)) when ageing out of the system, which
likely influence post-secondary education, employ-
ment, life skills and housing. Five of the six studies
included race/ethnicity and gender characteristics of
the sample. Three out of the five studies that reported

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race indicated that over 50% of the sample was
African-American or non-Caucasian (61% for
Uzoebo et al. (2008); 70% for Mares & Kroner
(2011); and 62% for Kroner & Mares (2011)). Petr
(2006) indicated that 55.5% of the sample identified
as Caucasian, while 33.3% identified as African-
American. Powers et al. (2012) also indicated a higher
percentage of Caucasian participants (50.8%) com-
pared to African-American participants (16.4%);
however, this sample included only participants that
used special education services, which influence the
race distribution of this study. The three studies that
indicated a higher percentage of African-American
participants also indicated a higher number of female
participants (63% for Uzoebo et al. (2008); 58% for
Mares & Kroner (2011); and 55% for Kroner &
Mares (2011)). Petr (2006) and Powers et al. (2012)
indicated that fewer than 50% of the sample identified
as being female (48.1% and 41%, respectively). Four
out of the six studies indicated an average length of
stay. Three of these four studies indicated that the
average length of stay was 12 months or less: 9.9
months (Mares & Kroner 2011), 10.2 months
(Kroner & Mares 2011) and 12 months (Powers et al.
2012). However, Uzoebo et al. (2008) indicated that
the average length of stay for this programme was 17
months. The differences in length of stay could result
from different requirements important to each of the
programmes.

Outcome measures

Educational attainment

Four out of six of the primary studies (Petr 2006;
Mares 2010; Mares & Kroner 2011; Powers et al.
2012) examined educational attainment. The most
common theme discussed is secondary education
completion (i.e. high school completion or obtaining a
GED).

Petr (2006) indicated that seven youth (26%)
reported being seriously behind in their educational
progress and goals, with only one participant currently
enrolled in a GED programme. Petr did not discuss
post-secondary education goals; however, in order to
complete post-secondary education, youth need to
finish high school or obtain a GED.This study utilized
focus groups, from which a theme of concern that an
increased number of placements often leads to a
decrease in educational attainment emerged. The
trend that youth not finishing high school would have
to re-take grade levels or enroll in a GED programme

surfaced during this study. The public school system
increased the likelihood of poor educational attain-
ment due to not offering credit for courses taken in
residential care, resulting in youth falling behind in
their educational pursuits.

Mares (2010) suggested that secondary education
support was among one of the most common services
available and offered to youth according to a group of
independent living service providers. Conversely,
however, financial support for college was among the
least common service available or offered according to
the same providers.

Mares & Kroner (2011) examined the differences in
outcomes based upon mental-health concerns. They
indicated that participants with mental-health prob-
lems were less likely to complete high school com-
pared to participants without mental-health concerns
(0.60 decreased odds). In addition, those participants
that stayed in the programme longer (even 1 month
longer) were more likely to have completed high
school (0.96 increased odds). In addition, students
who were older at admission into the programme were
more likely to complete high school (between 1.55
and 2.35 increased odds). The authors argued that
while having a mental-health problem is a risk factor
for not completing high school, spending more time in
the programme and being older before entering the
programme predicted a higher likelihood of finishing
high school despite the risk.

Powers et al. (2012) indicated that at post-
intervention, 38% of the intervention group and 28%
of the comparison group had completed their second-
ary education (via either a high school diploma or
equivalent). At 1-year follow-up, this number
increased to 72% for the intervention group and 50%
for the comparison group. Three youth (one compari-
son youth and two intervention youth) were partici-
pating in post-secondary education after the
intervention. This number increased, at 1-year follow-
up, to 26% (6 comparison youth and 10 intervention
youth) of youth attended post-secondary education
classes at least part-time.

Employment

Three of the six primary studies discussed employ-
ment outcomes: Petr (2006), Mares & Kroner (2011)
and Powers et al. (2012). Seven of the 19 youth in
custody (still in foster care; 37%) and 3 of the 9 out of
custody (emancipated; 38%) were working at paid
jobs in the community (Petr 2006). Of the three that
were out of custody, two were working part-time and

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attending college. The youth in custody tended to
work part-time jobs in areas such as food services.

Similar to the education results, Mares & Kroner
(2011) indicated that being at least 1 year older
when entering into the programme predicted higher
rates of employment (between 1.55 and 2.35
increased odds). Additionally, staying at least 1
month longer before exiting the programme and not
having a mental-health problem predicted a higher
rate of paid employment (1.10 and 0.460 increased
odds, respectively).

The Powers et al. (2012) study reported that 14%
of the intervention group and 19% of the compari-
son group worked paid jobs at baseline. At post-
intervention, 34% of the intervention group
indicated that they had a job that paid a wage or
salary. In contrast, the rate of paid employment
decreased slightly for the comparison group from
19% to 16%. At 1-year follow-up, 45% of the inter-
vention group compared to 28% of the comparison
group had jobs that paid wages or salaries, working
at least part-time.

Housing

Four of the six primary studies – Mares (2010),
Kroner & Mares (2011), Mares & Kroner (2011), and
Powers et al. (2012) – discuss housing/living status
after completion of the ILP. According to the percep-
tions of the service providers, housing assistance was
among the most helpful service offered to emancipat-
ing youth (Mares 2010). Conversely, the service pro-
viders identified two gaps in housing: affordable
housing and structured transitional housing.

Kroner & Mares (2011) examined housing place-
ments after the ILP and determined that three-fourths
of youth went into an independent level of care: living
by self (28%), living with a friend (13%) or living with
a relative (17%). Additionally, just over half of the
participants (55%) attained a status of independent
living. Mares & Kroner (2011) indicated that partici-
pants who were older when entering into the pro-
gramme were more likely to live independently
(between 1.55 and 2.35 increased odds). Specifically,
female participants were more likely to have inde-
pendent housing at discharge as well as participants
who reported being parents (two times more likely).
Participants who remained in the programme at least
1 month longer were more likely to have independent
housing (1.10 increased odds).

The Powers et al. (2012) study indicated that at the
time of enrolment in the study, all participants were in

foster care. At post-intervention, 63% of the partici-
pants were still in foster care, 6 participants were
adopted or reunited with their birth family, 14 partici-
pants were living with friends or a partner in their own
apartment, 1 participant had housing provided
through Job Corps and 2 (both in the comparison
group) participants identified as being homeless. At
1-year follow-up, 57% of the all the participants had
exited care, 15 participants reported being reunited or
adopted, 14 participants were living in their own
apartment (either with a friend or partner), 4 partici-
pants were residing in college dormitories, 1 partici-
pant was in military housing and 1 participant had
housing through Job Corps. In addition, 60% of the
comparison group reported having a different place-
ment from the year before compared to 50% in the
intervention group.

Mental health/special needs

Mares & Kroner (2011) and Powers et al. (2012) are
the two primary studies that examine outcomes for
youth with mental-health problems or special educa-
tion needs. Mares & Kroner (2011) indicated that
youth with mental-health problems are less likely to
complete high school (0.61 decreased odds), find
employment (0.64 decreased odds) or establish inde-
pendent housing (0.68 decreased odds). Powers et al.
(2012) indicated that participants who received the
intervention fared better in high school completion
(38% compared to 26% at completion of pro-
gramme and 72% compared to 50% at 1-year
follow-up) compared to participants in the compari-
son group. In addition, participants in the interven-
tion group fared better in employment outcomes
(14% compared to 19% at baseline, 34% compared
to 16% at completion of the programme, and 45%
compared to 28% at 1-year follow-up) compared to
the comparison group.

Life skills

Three of the six primary studies – Petr (2006),
Uzoebo et al. (2008) and Mares (2010) – reported life
skills outcomes. Petr suggested that 26% (n = 7 out of
19) of youth indicated that they had not received
any life skills training, while 2 (10%) out of 19 par-
ticipants indicated it was offered, but they refused.
The participants that received life skills training
(63%) indicated that they were in one of three set-
tings: a regular school setting, a mental-health agency
or in a group home facility. In addition, participants

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also indicated receiving life skills training from foster
parents in a less formal, day-to-day, basis. There were
inconsistent reviews of the formal training sessions;
some participants suggested they learned a great deal,
whereas others insisted that the classes were boring
and covered material already known.

Uzoebo et al. (2008) indicated that ILP partici-
pants reported higher mastery of skills in the areas of
daily living skills, work life, money management and
budgeting, and self-care. In addition, at follow-up,
participants demonstrated an increase in skills acqui-
sition (52.6% at intake to 55.2%). Participants indi-
cated that receiving training in a classroom setting
was less efficacious to learning via a mentor or from
‘real life experiences’. In addition, participants sug-
gested that modelling life skills was more productive
and significant compared to learning about life skills
in a classroom setting. Participants indicated a need
for a curriculum that was hands-on and age appro-
priate. In the Mares (2010) study, life skills training
was identified as being among the most helpful for
emancipating youth; however, hands-on independent
living life skills training was identified as being a gap
in services provided, viewed as both the most helpful
independent living service and among the greatest
unmet need.

D I S C U S S I O N

This review aimed to assess the effectiveness of ILPs
in yielding productive outcomes (i.e. educational
attainment, employment and housing) for YAO.
There is a lack of consistency in the types of services
provided by ILPs as well as no general standards
that all ILPs follow (Courtney 2005; Naccarato &
DeLorenzo 2008; Avery 2010). Therefore, some
ILPs focused on housing issues but overlooked edu-
cational attainment or employment, while other ILPs
focused solely on secondary educational attainment
but overlooked post-secondary educational services.
Only four out of six studies reported outcomes for
education, and each reported educational attainment
differently. For example, Petr (2006) reported infor-
mation regarding educational progress and goals as
well as participant likelihood to finish high school or
enroll in a GED programme. Mares (2010) reported
the information regarding ILP service providers in
the role of secondary education support and finan-
cial support for post-secondary education. Mares &
Kroner (2011) reported completion rates for high
school among participants hindered by mental-
health problems. Finally, Powers et al. (2012)

reported on completion rates for high school as well
as entrance rates for post-secondary education;
however, they focused only on youth with mental-
health concerns enrolled in special education
programmes.

In addition to the inconsistencies in reporting edu-
cational outcomes, inconsistencies in reported out-
comes of employment also existed; only half of the
primary studies discussed employment outcomes.
Petr (2006) and Powers et al. (2012) reported the
percentage of participants that worked jobs that paid
a salary; however, the Powers et al. (2012) study was
the only one to discuss follow-up rates. While the
studies reported outcomes similarly, only three
studies addressed this outcome measure, and
employment is important for YAO seeking independ-
ent living (Donkoh et al. 2006; Mares & Kroner
2011; Unrau et al. 2011). In addition, there was a
lack of information regarding the type of jobs the
youth receive, how long youth maintain or stay in
one job and the typical salary these youth receive.
These aspects of employment are important for
policy implications as well as help ascertain the pre-
paredness of youth leaving the ILP and succeeding
in independent living.

Independent living is an important outcome as four
out of the six primary studies addressed housing out-
comes. However, the housing outcomes were not
reported in a consistent manner across studies. For
example, two out of the four studies (Mares 2010;
Mares & Kroner 2011) reported this outcome in
terms of perception of the importance of housing
assistance and housing outcomes of participants with
mental-health problems compared to youth without
mental-health problems. The other two studies
(Kroner & Mares 2011; Powers et al. 2012) reported
percentage of participants’ actual housing situations.
Additionally, a lack of reporting the different aspects
of housing existed. For instance, only one of the
studies (Kroner & Mares 2011) indicated what type of
housing these youth received after the ILP (i.e. low
income, apartment, condominium, renting a house,
owning a house and living with a relative). Powers
et al. (2012) reported basic long-term follow-up of
housing; however, none of the other studies reported
follow-up results of housing (i.e. stability in housing,
stability of the environment, stability of the neigh-
bourhood).

Only two out of six studies addressed mental-health
concerns or special education needs (Mares & Kroner
2011; Powers et al. 2012).These studies examined how
having a mental-health concern or special education

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need could influence other outcomes (i.e. educational
attainment, employment, housing and life skills). Lit-
erature suggests that foster youth fall behind in
education because of the pressure and instability of the
foster care system, which can lead to possible mental-
health concerns (Lemon et al. 2005; Montgomery
et al. 2006; Collins et al. 2008; Avery & Freundlich
2009; Avery 2010; Harder et al. 2011; Unrau et al.
2011). However, only two of the six primary studies
examined this outcome.

The final outcome examined in this review was life
skills. Again, only half of the primary studies reported
life skills outcomes. Many ILPs interpret and address
life skills and life skills training differently. Petr (2006)
and Uzoebo et al. (2008) reported percentage of par-
ticipants’ knowledge of life skills. Uzoebo et al. (2008)
discussed life skills in terms of daily living (i.e. work life,
money management and budgeting, and self-care). In
the Mares (2010) study, life skills were identified as
being important for youth exiting care; however, often
life skills are taught and discussed in a classroom or
controlled setting. This was a concern for Petr (2006)
and Uzoebo et al. (2008) as well, who indicated that
many participants preferred a less formal, hands-on
approach. In addition, the participants enrolled in the
Uzoebo et al. (2008) study indicated that the use of
mentors would benefit the participants, allowing the
mentors to model life skills in order for the participants
to learn from real-life experiences.

While these six studies are important, they bring up
some limitations that need addressing. For example,
not one of the studies used an RCT, which can better
determine the effectiveness of an intervention.
However, evaluating ILPs utilizing RCTs does not
commonly occur (Montgomery et al. 2006). Four of
the six primary studies utilized some comparison
group, which allows for an increased understanding as
well as a more confident assumption regarding the
effectiveness of particular ILPs (Uzoebo et al. 2008;
Kroner & Mares 2011; Mares & Kroner 2011; Powers
et al. 2012).Two of the studies utilized mixed methods
(using both qualitative and quantitative methodology;
Uzoebo et al. 2008; Mares 2010).The inclusion of one
study that utilized a non-experimental design and four
others that utilized quasi-experimental designs indi-
cates insufficient experimental outcome studies for
ILPs.

Limitations

While this review of ILPs calls attention to the lack in
consistency in ILPs for youth exiting the care system, it

only included published studies in peer-reviewed jour-
nals. Grey literature, studies published in a different
language and studies published prior to 2006 were not
included in the review. Although this is a limitation,
peer-reviewed studies are often highly regarded com-
pared to all other literature. In addition, while includ-
ing studies in different languages or in different
countries can be illuminating, US policy-makers would
most likely be interested in the studies published in the
USA.

Next steps

The present review determined that insufficient
outcome studies evaluating ILPs for YAO still exist.
Future studies evaluating the outcomes of ILPs need
to make use of stronger research designs, such as
RCTs and high-quality quasi-experimental designs.
While the RCT does not necessarily need to be inter-
vention vs. no intervention (e.g. the RCT could be
new ILP vs. standard ILP), the use of an RCT will
expand the understanding of which ILP works better
and potentially what types of interventions work
better. For example, YAO may need more housing
support and less employment support in order to have
a sustainable lifestyle. However, this assumption
cannot be made from the literature that currently
exists. The most that can be said about the effective-
ness of ILPs is that YAO who participate in ILPs
obtain some productive outcomes. Comparisons
about non-ILP youth cannot be made, comparison
between certain ILPs cannot be made, and claims that
ILPs effectively aid YAO to obtain productive out-
comes cannot be made.

YAO are at an increased risk in terms of educa-
tional attainment, employment, housing, mental
health and life skills, yet the ILPs, which are adver-
tised as aiding YAO in attaining productive out-
comes, have failed to use methodologies that can
adequately report on the outcomes. It is a disservice
to these youth to inflate desired outcomes with
documented, achieved results. Future studies also
need to focus on comparative studies, which examine
the effectiveness of different ILPs. It would also be
desirable to promote some common core outcome
measures to be used in evaluating ILP results, stand-
ardized ways of reporting educational, employment
and housing outcomes, for example. There are great
groundwork studies out there, some included in this
review; however, there needs to be a next step.
Future studies need to include RCTs and compari-
son studies in order to explicate the effectiveness of

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ILPs. Only then can policies be modified and cor-
rected to fit the needs of the population.

R E F E R E N C E S

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(AFCARS). (2011) Preliminary FY 2011 Estimates as of July

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(accessed June

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children: the policy context. The Future of Children, 14, 49–
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Avery, R.J. (2010) An examination of theory and promising

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family ties: implications for youth transitioning from foster

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ent living programmes for improving outcomes for young

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level of care among clients discharged from a scattered-site

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to young adulthood: the role of independent living programs in

supporting successful transitions. Children and Youth Services

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living program: predictors of client outcomes at discharge.

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pendent living programs for young people leaving the care

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© 201 John Wiley & Sons LtdChild and Family Social Work 2017, 22, pp 515–5265 526

http://www.acf.hhs.gov/sites/default/files/cb/afcarsreport19

http://www.acf.hhs.gov/sites/default/files/cb/afcarsreport19

http://wispolitics.com/1006/Chapin_Hall_Executive_Summary

http://wispolitics.com/1006/Chapin_Hall_Executive_Summary

http://www.manhattan-institute.org/html/cr_baeo.htsm#14

http://www.manhattan-institute.org/html/cr_baeo.htsm#14

Copyright of Child & Family Social Work is the property of Wiley-Blackwell and its content
may not be copied or emailed to multiple sites or posted to a listserv without the copyright
holder’s express written permission. However, users may print, download, or email articles for
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8Residential and
Institutional
Placement of

Juveniles

Comstock/Thinkstock

Learning Objectives

After studying this chapter, you should be able to accomplish
the following objectives:

Summarize the history behind the residential

placement of youth.

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During 2008, a juvenile correctional center in Ohio lost over half of its staff. The center, called Marion Juvenile
Correctional Facility, saw a significant increase in violence among residents of the facility. In fact, according to
the Columbus Dispatch,

Assaults on staff members have resulted in a broken nose, a slash across the face, choking,
unconsciousness, bites, a blown-out knee and the indignity of being doused with milk cartons filled
with urine. Guards, teachers and other prison workers regularly are assaulted. Last year, they missed
the equivalent of seven years of workdays because of injuries and disabilities. Large youth fights have
sent staff members to the hospital four, five, six at a time. Slightly more than half of the frustrated,
frightened and fatigued guards quit last year, some walking away from $15.80-an-hour jobs after only
a few days. (Ludlow, 2008)

As with any situation, the causes of violence are varied; however, reports indicated that gang violence and
understaffing all contributed to the situation at the Marion Juvenile Correctional Facility. The state was hit with
a federal lawsuit after evidence of widespread abuse by staff surfaced. As a result, correctional staff members
were trained to use less force when managing unruly youth. However, as noted by the unions representing
correctional officers, the hands-off policy created concerns for correctional officers, who indicated they felt
unsafe at the facility.

At the time the Columbus Dispatch article was written in early 2008, the department director expressed optimism
about being able to turn around the correctional facility. The director noted that staff training would help to
reduce use-of-force incidents against youth. In addition, the facility worked to identify gang-involved youth and

Define confinement and who is most likely to be

sentenced to institutions for juveniles.

Explain the different types of short-term residential

placements for youth.

Describe the advantages and disadvantages of group

homes.

Explain the degree of effectiveness of wilderness

camps.

Identify the different types of short-term residential

placements for youth.

Summarize the issues associated with long-term

secure correctional facilities.

Describe the risks involved with confining juveniles

in adult facilities.

Identify the components of successfully helping

juveniles reintegrate into society after release.

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transfer them to other facilities. Just one year later, on January 8, 2009, the Ohio Department of Youth Services
issued a press release announcing the closure of the Marion Juvenile Correctional Facility.

Fast forward to today. Ohio has made great strides to reform its juvenile justice system. Since 2008, the juvenile
justice population residing in youth centers has declined from 1,700 youth to 429. The state also created the
Reentry Continuum, an innovative plan that relies on best practices in rehabilitation. The plan calls for a number
of principles that guide Ohio’s approach toward managing youth in the juvenile justice system:

Adopt the Effective Practices in Community Supervision (EPICS) model for parole staff
Implement risk and need assessment tools to assign treatment programming
Reduce the length of time on parole for low and low-moderate risk youth by collaborating with judges
Support reentry courts at the county level
Develop discharge plans to assist youth with any needed services post-release

The Reentry Continuum is just one example of major reforms that states nationwide have adopted to reduce the
number of youth in custody.

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8.1 Introduction

Confining juveniles as a form of punishment is not without controversy. Throughout this text, we have discussed
how shifts in policy are often influenced by the social climate of the time. Not surprisingly, when it comes to
confining juvenile offenders as a form of punishment, we have seen (and continue to see) shifts in policy. For
example, there was an increase in the use of confinement for juveniles during the get-tough era of the 1980s and
1990s. Since that time, however, states have reduced by nearly half the population of youth confined. The recent
shift is due to several factors. For one thing, the cost of confinement has forced states to rethink their policies.
Moreover, there is a growing recognition that confinement can exacerbate rather than solve the problems that
bring youth to the juvenile justice system. Even so, the confinement of juveniles has a long history and is
unlikely to be abandoned in the near future.

The use of confinement has often been justified on the grounds of deterrence. For example, although probation is
the most widely used sanction for juveniles, there has always been a concern that the general public views
probation as merely a slap on the wrist. From a deterrence standpoint, justice should be swift, certain, and just
severe enough to outweigh the benefits of crime. Using the biblical reference “to spare the rod is to spoil the
child,” some observers argued during the 1990s and early 2000s that only after delinquents experienced the harsh
hand of justice in the form of boot camps, chain gangs, or confinement would they think twice about committing
crime in the future. Policymakers argued that the firm hand of justice would steer youth onto the right path.

Over the past decade, there has been a groundswell of support for reducing the use of confinement for juveniles.
For example, it has been argued that institutions for juveniles act as “crime schools,” as youth from various
criminal backgrounds come together and reinforce their criminal status. In these situations, juveniles can learn
how to commit other crimes from fellow juveniles. Second, there are concerns about the physical and emotional
effects of confinement on youth who are still developing and growing. In particular, youth could be traumatized
by their confinement experiences. Third, there are concerns regarding inequality in terms of who is placed in
these settings. In particular, girls appear to be more likely to be sent to facilities for minor charges, and African
American youth are disproportionately represented. Finally, critics contend that juveniles sentenced to serve time
in adult facilities do worse than those who remain in the juvenile system. The complexity of these issues cannot
be underestimated. We will discuss these and other issues in this chapter as we examine the impact and
effectiveness of institutional placement for juveniles.

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Out-of-home placements
for juveniles range from
detention centers to group
homes to residential
treatment centers.

Comstock/Thinkstock

8.2 Defining Confinement for Juveniles

The words confinement or institutional placement often conjure an image of a
large, concrete prison with bars and barbed wire. These images of prison have
been popularized by movies such as Shawshank Redemption and Dog Pound, and
television shows such as Orange Is the New Black and Empire. Although media-
derived images of prison may be accurate for some maximum-security adult
prisons, juvenile facilities are more varied and complex. The terminology used to
define juvenile facilities varies so greatly that the terms residential or out-out- of- of-
homehome placements placements are often used rather than the term prison. In fact, according
to Melissa Sickmund (2010),

Juvenile facilities are known by many different names across the country:
detention centers, juvenile halls, shelters, reception and diagnostic centers,
group homes, wilderness camps, ranches, farms, youth development centers,
residential treatment centers, training or reform schools, and juvenile
correctional institutions. (p. 1)

The lack of a standard definition for these facilities can lead to a great deal of
confusion. For example, to examine whether residential placement or community
placement is more effective in reducing recidivism among youth, we would need
to make sure we are not comparing apples to oranges. We would also need to
decide how to measure or quantify residential placement. We would expect, for
example, that a juvenile placed in a wilderness camp would be exposed to a
different set of experiences than a juvenile placed in a secure correctional facility.
In an effort to identify these differences and the impact they have on the behavior of juveniles, we will examine
each of these settings in detail in subsequent sections. First, though, let’s look at the broad data on which and how
many juveniles are in these facilities, with the understanding that the common thread among all of these facilities
is that juveniles reside at the facility rather than in their homes.

Population Characteristics of Residential Facilities

The Office of Juvenile Justice and Delinquency Prevention (OJJDP) conducts a census of residential facilities for
juveniles every other year. The results of the 2016 survey indicated that 45,567 juvenile offenders were held in
juvenile residential facilities, representing a decline of more than 58% since 2000 (Puzzanchera, Hockenberry,
Sladky, & Kang, 2018). Table 8.1 illustrates that the majority of juvenile facilities are labeled “residential
treatment centers.” Those facilities most similar to what we consider a “prison” in adult terms are labeled “long-
term secure correctional facilities.” Table 8.1 indicates that there are 189 of these facilities across the country.

Table 8.1: The number of residential juvenile facilities by type, 2016

Detention
center Shelter

Reception/diagnostic

center

Group
home

Ranch/wilderness
camp

Long-
term

secure

Residential
treatment

center

Number
of

662 131 58 344 30 189 678

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facilities
Source: From “Table: Year by facility self-classification, United States,” in Juvenile residential facility census databook: 2000–2016, by C. Puzzanchera,
S. Hockenberry, T. J. Sladky, and W. Kang, 2018, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s ph t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s p
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / s e l e c t i o n _ p ro fil e . a s p )

By examining the latest trends, we see in Figure 8.1 that the number of juveniles in residential placement has
declined significantly. This decline is not surprising, since as we discussed in Chapter 1 the overall arrest rates
among youth have also declined significantly.

Figure 8.1: Juveniles in residential placement, 2000 and 2014

From “Table: Number of facilities and juvenile offenders by facility size, United States (for years 2004 and
2014),” in Juvenile residential facility census databook: 2000–2016, by C. Puzzanchera, S. Hockenberry, T. J.
Sladky, and W. Kang, 2018, Retrieved from
h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s ph t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s p
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / j r f c d b / a s p / d i s p l a y _ p ro fil e . a s p )

As seen in Table 8.2, the number of juveniles in residential placement varies quite a bit by state. For example,
Table 8.2 lists both the number of juveniles in placement (for 2015) and the rate of placement. The rate of
placement is the number of juveniles in custody per 100,000 youth. A rate helps to account for differences in
state population. In other words, we would expect that California would have more juveniles in custody, given
that it is the most populous state in the country. However, in the case of California, we see the placement rate of
165 is below that for many other states. Six of the most populous states—California, Texas, Florida, New York,
Pennsylvania, and Ohio—have reduced their placement rates by nearly half since 1997 (Hockenberry, 2018).

Table 8.2: The number of juveniles in residential placement by state, 2015

State where offense occurred (upper
age of juvenile court jurisdiction in
2015)

Number of juvenile offenders in
public or private residential
placement, 2015

Residential placement
rate, 2015 (per 100,000
youth)

U.S. total 48,043 152

https://www.ojjdp.gov/ojstatbb/jrfcdb/asp/selection_profile.asp

https://www.ojjdp.gov/ojstatbb/jrfcdb/asp/display_profile.asp

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Alabama (17) 849 168

Alaska (17) 207 262

Arizona (17) 717 98

Arkansas (17) 555 175

California (17) 6,726 165

Colorado (17) 999 177

Connecticut (17) 141 38

Delaware (17) 162 176

District of Columbia (17) 105 251

Florida (17) 2,853 153

Georgia (16) 1,110 111

Hawaii (17) 51 39

Idaho (17) 393 200

Illinois (17) 1,542 112

Indiana (17) 1,563 217

Iowa (17) 675 207

Kansas (17) 564 177

Kentucky (17) 510 112

Louisiana (16) 831 193

Maine (17) 81 67

Maryland (17) 612 101

Massachusetts (17) 426 66

Michigan (16) 1,554 172

Minnesota (17) 852 149

Mississippi (17) 243 74

Missouri (16) 948 173

Montana (17) 171 170

Nebraska (17) 465 225

Nevada (17) 627 209

New Hampshire (17) 69 54

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New Jersey (17) 636 69

New Mexico (17) 363 164

New York (15) 1,386 99

North Carolina (15) 468 60

North Dakota (17) 144 203

Ohio (17) 2,163 178

Oklahoma (17) 552 131

Oregon (17) 1,113 286

Pennsylvania (17) 2,826 228

Rhode Island (17) 198 200

South Carolina (16) 693 161

South Dakota (17) 228 254

Tennessee (17) 660 97

Texas (16) 4,299 153

Utah (17) 453 114

Vermont (17) 27 47

Virginia (17) 1,227 147

Washington (17) 921 130

West Virginia (17) 567 329

Wisconsin (16) 762 147

Wyoming (17) 177 296
Source: From “Table: In 2015, the national commitment rate was twice the detention rate, but rates varied by state,” in Juveniles in residential
placement, 2015, by S. Hockenberry, 2018, Retrieved from h t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d fh t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d f ( h t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d f ) ( h t t p s : / / w w w. o j j d p . g o v / p u b s / 2 5 0 9 5 1 . p d f )

The types of offenses that lead to residential placement are shown in Figure 8.2. Person offenses, which include
violent offenses such as murder and robbery, represent the largest category, with the second largest category
being property offenses. In fact, 60% of the juveniles in residential placement were there as a result of a person
or property offense.

Figure 8.2: Percentage of juveniles in any
residential setting by offense type, 2015

https://www.ojjdp.gov/pubs/250951

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From “Table: Year of census by most serious offense general,” in Easy
access to the census of juveniles in residential placement: 1997–2015, by
M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera, 2017, Retrieved
from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s ph t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p )

If we examine gender, we can see in Figure 8.3 that 85% of youth in residential placement are boys. What this
doesn’t illustrate, however, is that girls of color are more likely than white girls to be placed in a residential
setting. Girls are also more likely to be placed in residential settings for lower level offense. According to the
latest statistics available from the OJJDP (Sickmund, Sladky, Kang, & Puzzanchera, 2017), more than half of
youth placed in residential settings for running away are girls.

Figure 8.3: Percentage of juveniles in
residential placement by gender, 2015

Eighty-five percent of the youth in
residential placement were boys.

From “Table: Year of census by sex,” in Easy access to the
census of juveniles in residential placement: 1997–2015, by
M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera,

https://www.ojjdp.gov/ojstatbb/ezacjrp/asp/display.asp

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2017, Retrieved from
h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l ah t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a
y. a s p ?y. a s p ?
ro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p &ro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p &
e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?
ro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p & e x p o r t _ fro w _ v a r = v 0 1 & c o l _ v a r = v 0 2 & d i s p l a y _ t y p e = ro w p & e x p o r t _ f
i l e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 )i l e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 )

If we examine race, we see that there are differences overall and by state. Table 8.3 illustrates that the total
percentage of minority youth in custody in the United States is higher (42% black, 22% Hispanic) than for white
youth (31%). The table also illustrates differences by state. Table 8.3 shows 17 states where 50% or more of the
population under state custody is black. The jurisdictions with the highest rates include District of Columbia
(97%), Delaware (80%), Louisiana (80%), Maryland (79%), Mississippi (77%), Georgia (74%), and New Jersey
(72%). What is difficult to assess from the table is the extent to which these percentages represent
disproportionality.

Table 8.3: Percentage under state custody by race/ethnicity, 2015

State of offense White Black Hispanic1 American Indian2 Asian Other

U.S. total 31% 42% 22% 2% 1% 2%

Alabama 35 60 3 0 0 1

Alaska 38 14 1 36 1 10

Arizona 33 16 36 8 1 7

Arkansas 36 57 6 0 1 1

California 13 28 55 1 2 1

Colorado 36 21 39 1 1 1

Connecticut 23 47 26 0 0 4

Delaware 13 80 7 0 0 2

Dist. of Columbia 0 97 0 0 0 0

Florida 29 62 9 0 0 0

Georgia 18 74 5 0 1 2

Hawaii 18 0 6 0 53 29

Idaho 70 2 23 2 2 1

Illinois 21 63 14 0 0 1

Indiana 53 36 7 0 0 4

Iowa 56 29 9 2 1 2

Kansas 46 33 19 1 1 1

https://www.ojjdp.gov/ojstatbb/ezacjrp/asp/display.asp?row_var=v01&col_var=v02&display_type=rowp&export_file=&printer_friendly=&v0110=v0110

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Kentucky 56 34 2 0 0 8

Louisiana 17 80 1 1 0 1

Maine 78 15 0 4 0 4

Maryland 14 79 6 0 0 0

Massachusetts 23 30 41 0 1 6

Michigan 40 47 6 1 0 6

Minnesota 38 40 7 10 2 4

Mississippi 22 77 0 0 0 1

Missouri 49 44 3 0 0 3

Montana 54 12 12 16 0 5

Nebraska 40 25 23 5 1 5

Nevada 25 37 31 2 2 3

New Hampshire 78 9 9 4 0 4

New Jersey 8 72 18 0 0 0

New Mexico 14 7 74 4 0 2

New York 28 52 16 1 1 2

North Carolina 21 67 7 2 0 3

North Dakota 54 13 4 25 0 4

Ohio 42 50 3 0 0 4

Oklahoma 39 40 8 11 0 2

Oregon 56 13 24 4 1 1

Pennsylvania 29 53 14 0 0 3

Rhode Island 32 30 32 0 3 3

South Carolina 32 48 16 1 0 3

South Dakota 49 4 3 39 1 3

Tennessee 46 41 9 0 0 3

Texas 21 34 44 0 0 1

Utah 50 9 34 5 2 1

Vermont 89 11 0 0 0 0

Virginia 24 62 11 0 0 3

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Washington 43 22 20 6 2 7

West Virginia 84 8 2 1 0 5

Wisconsin 28 56 9 3 1 2

Wyoming 66 7 14 12 0 2
1The Hispanic category includes person of Latin American or other Spanish culture or origin regardless of race.
2American Indian includes Alaskan Natives; Asian includes Pacific Islanders.
Source: From “Table: Race/ethnicity by state, 2015,” in Easy access to the census of juveniles in residential placement: 1997–2015, by M. Sickmund, T.
J. Sladky, W. Kang, and C. Puzzanchera, 2017, Retrieved from h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ?h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ?
s t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro ws t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro w ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ? ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / S t a t e _ R a c e . a s p ?
s t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro w )s t a t e = & t o p i c = S t a t e _ R a c e & y e a r = 2 0 1 5 & p e rc e n t = ro w )

Disproportionate Minority Contact (DMC)

The rate of confinement for minority populations has led to a number of initiatives, most notably the
Disproportionate Minority Contact (DMC) initiative designed to reduce the number of minorities who come
in contact with the system. According to the Juvenile Justice and Delinquency Prevention Act of 2002, states
receiving formula grants are required to address the issue of overrepresentation of minorities at each stage of the
juvenile justice system, which includes institutions. The OJJDP has become a leader in collecting data to
examine the national rates of contact. As an example of this leadership, they developed the National
Disproportionate Minority Contact Databook (see https://www.ojjdp.gov/ojstatbb/dmcdb/
(https://www.ojjdp.gov/ojstatbb/dmcdb/) ).

Data from this source are referred to as the Relative Rate Index (RRI). The RRI assesses the levels of
disproportionate minority contact at various stages of juvenile justice system processing at the national level.
This rate helps us understand the extent of disproportionality by taking into account the population size of
different minority groups (e.g., black, Hispanic, Asian, American Indian) in the United States. The rate
calculated is compared to the rate for white youth. The OJJDP created the RRI matrix to help states and
jurisdictions measure levels of disparity within different parts of the juvenile justice system. By capturing the
extent of disproportionate minority contact within communities, stakeholders can identify decision points that
may need policy reforms. These data now allow us to examine trends over time.

Figure 8.4 illustrates that, with the exception of Asian American youth, all other minority youth have a rate of
placement in residential settings that is higher than for white youth. Black and Hispanic youth have the highest
rates of placement compared to other groups.

Figure 8.4: Relative rate index for youth receiving residential
placement, 2005–2015

https://www.ojjdp.gov/ojstatbb/ezacjrp/asp/State_Race.asp?state=&topic=State_Race&year=2015&percent=row

https://www.ojjdp.gov/ojstatbb/dmcdb/

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*AHPI: Asian, Hawaiian, or Pacific Islander

**AIAN: American Indian or Alaskan Native

From “Relative rate indices of adjudication and placement of delinquency referrals,” in National
disproportionate minority contact databook, by C. Puzzanchera and S. Hockenberry, 2018, Retrieved from
h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ?h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ?
d i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e sd i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e s
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ?( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / d m c d b / a s p / d i s p l a y _ t re n d . a s p ?
d i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e s )d i s p l a y _ i n = 1 & p o i n t = 9 & o f f e n s e = 1 & d i s p l a y t y p e = r r i & s h o w _ c h a r t = y e s )

https://www.ojjdp.gov/ojstatbb/dmcdb/asp/display_trend.asp?display_in=1&point=9&offense=1&displaytype=rri&show_chart=yes

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8.3 Short-Term Residential Facilities

Several different types of facilities are referred to as short-term residential facilities, including detention centers,
reception/diagnostic centers, and youth shelters. Detention centers provide a temporary form of confinement
typically used before the intake or adjudication phase. The police may decide to detain youth who pose a risk to
themselves or others. In addition, if the police are unable to locate a youth’s parents or guardians, they may place
the juvenile in detention until the responsible party can be located.

Reception/diagnostic centers typically house youth for short periods while correctional officials assess the
juveniles’ needs in order to determine the best placement. The process is similar to the intake process; however,
two characteristics distinguish it from the traditional intake process. First, unlike the intake process in which a
youth may meet with a probation officer in the community, youth remain confined during this assessment
process. Second, the assessment of youth at this stage often occurs once the youth has been adjudicated as
delinquent and has been remanded to serve time in a residential facility (Sickmund, 2010). For example, in Ohio,
all youth committed to the Department of Youth Services are sent to one reception center to be assessed for
placement in one of the state’s secure juvenile correctional facilities.

Youth shelters are another example of a short-term residential facility. Shelters are designed to provide short-
term placement for youth who cannot be immediately returned to their families. Although designed primarily to
serve status offenders and abuse and neglect cases, youth shelter care facilities can also serve delinquent youth if
a detention center bed is unavailable. Most youth spend only days at youth shelters; however, the stay can be
extended to weeks if the court finds placement to be difficult. Some youth shelters provide extensive services
(e.g., psychological counseling, educational services), whereas others simply provide temporary supervised
housing (Hicks-Coolick, Burnside-Eaton, & Peters, 2003).

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8.4 Group Homes

Group homes may be either short or long term, and they can serve a variety of youth in the juvenile justice
system. The typical group home concept provides supervision and services in a home-like setting. Group homes
tend to be smaller than other residential facilities, typically serving 15 or fewer youth at any given time. The
facility is considered nonsecure (e.g., no barbed wire or other security precautions) but does have locked doors,
and youth who leave without permission may be punished.

Group homes vary in terms of both the population they serve and the services they offer. In terms of the
population served, juvenile group homes accept those adjudicated as delinquent and abuse and neglect cases.
They can serve both boys and girls, although coed facilities are rare. In some states, group homes can be used as
halfway houses for youth released from long-term secure facilities. In terms of services, group homes can
provide myriad services and programs. For example, youth residing in these group homes may be able to leave
the home to attend school or outpatient therapy at a treatment center. Other times, therapy groups can be run at
the facility itself with all of the residents of the home (Farmer, Siefert, Wagner, Burns, & Murray, 2017).

Because the services at the facilities vary greatly, assessing their effectiveness is difficult. The most well-known
group home, called Boys Town, was established in 1917 by Father Flanagan, a Catholic priest in Omaha,
Nebraska. The program, which was featured in a motion picture by the same name in 1938, is described in the
accompanying Spotlight.

Spotlight: Boys Town

The Boys Town concept evolved out of Father Flanagan’s concern for abused and neglected children. The
original Boys Town program was an orphanage for young boys (Friman et al., 1996). Today, the nonprofit
organization runs treatment programs in nine states and, according to its website (boystown.org
(https://www.boystown.org/Pages/default.aspx) ), provides services for 1.6 million children per year. The
programs serve both boys and girls.

The Boys Town program maintains its original focus on abused and neglected juveniles, targeting at-risk
youth in an effort to make them productive members of society. Father Flanagan’s original goal was to
help abused and neglected boys to be productive citizens by providing them with opportunities to work in
a loving home. Today’s program has expanded to serve at-risk youth who are not necessarily in the foster
care system but need services. According to the organization’s website, the program has five objectives:

Teaching children and families life-changing skills
Helping children and families build healthy relationships
Empowering children and families to make good decisions on their own
Caring for children in a family-style environment
Supporting children and families in religious practices and values

Boys Town operates a number of different types of programs in different settings. Typical services
include the following:

Residential treatment programs
Group homes

https://www.boystown.org/Pages/default.aspx

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In-home family care programs
Foster care
Community support and mentoring programs

Studies suggest that the services offered, particularly the residential treatment centers, are effective in
increasing independent living skills, family functioning, and healthy relationship development (Friman et
al., 1996).

Boys Town has been studied more extensively than other group homes across the country. Results of those
evaluations find positive results (Kingsley, Ringle, Thompson, Chmelka, & Ingram, 2008); however, other
studies suggest that group homes without treatment do not produce long-term change in youth (Barth, 2005).
Why are the results mixed? It is likely the results are different because measuring the efficacy of Boys Town
group homes suffers from the same problem that we have in assessing all juvenile facilities: the group homes
even within Boys Town are quite varied. For example, some homes simply provide supervised housing, whereas
others may provide more extensive services. A home that is simply a residential setting for the youth, without
treatment services designed to address their issues, is less likely to have an impact.

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Wilderness camps are more effective
when treatments such as therapy are
used in addition to challenging
outdoor activities.

Paul M. Walsh/The Leader Telegram/Associated
Press

8.5 Wilderness Camps/Ranches

Wilderness camps, also known as wilderness ranches, became popular in the 1960s and 1970s. These camps
attempt to shape the self-efficacy of youth by exposing them to challenging situations. For example, youth may
be asked to complete a ropes course or a hiking expedition. They may also be asked to camp outdoors for a
period of time and use survival skills to build a fire, find shelter, and cook their own food. Challenging troubled
youth to overcome certain physical challenges is thought to increase their belief in themselves and their ability to
reach their goals. The idea of building self-efficacy through direct experiences is the foundation of the
experiential learning approach, which is the act of learning through doing. Engaging in physical activities to
learn a concept rather than more passive strategies such as reading a book will provide youth with a different set
of experiences (Kolb, 1984).

VisionQuest, a national nonprofit organization that began offering a variety of services to juvenile delinquents in
the 1970s, is most well known for its outdoor programs. One of the more interesting programs is referred to as
the Wagon Train. According to the organization’s website (www.vq.com (http://www.vq.com//) ), the Wagon Train
program “gives troubled youth the extraordinary experience of traveling cross-country for an extended period of
time via horseback and covered wagon.” Youth in the program are required to take care of the horses pulling the
wagons and to set up camp each night. The website touts the program as one that provides outdoor experiences
that mold the character of wayward youth.

Wilderness camps typically last from several weeks to months. For
example, a highly structured wilderness camp in Florida called the
Florida Environmental Institute targets youth adjudicated of felony
charges by the Department of Juvenile Justice in Florida. Nicknamed
the “Last Chance Ranch,” the program is in a remote area of the
Florida Everglades, which makes escape nearly impossible.
Participants typically stay at the ranch for 12 months and during that
time assist with raising pigs, cattle, horses, and various crops in
addition to engaging in more traditional activities such as educational
programs and mental health and substance abuse treatment. Youth are
required to progress through four phases to eventually obtain release.
Although evaluations of the program are not available, its founders
argue the work ethic builds character among youth.

Are wilderness camps effective at reducing recidivism among
delinquent youth? Unfortunately, studies suggest that the core
foundation of a wilderness camp (e.g., challenging outdoor activities)
is not sufficient to influence recidivism rates in delinquent youth. A
review of the literature by Sandra Jo Wilson and Mark Lipsey (2001)
concluded that wilderness-based camps simply focused on physically
challenging situations are not effective in reducing recidivism.
However, they did find that some programs were more effective if they
added treatment services such as family, individual, and group therapy
(Wilson & Lipsey, 2001). Other studies have also concluded that the
treatment services were the reason for the reductions in recidivism, not
the structure of the camp (MacKenzie, Gover, Styve, & Mitchell, 2000). The question becomes, then, if the only
way to make wilderness programs more effective is to add treatment groups, do the physical challenges have any

http://www.vq.com//

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beneficial effect? There is no definitive answer yet, but it appears increasingly unlikely that the physical
challenges are beneficial.

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Youths housed in residential treatment centers
attend group-based treatment programs for
their specific issues.

Spencer Grant/age fotostock/SuperStock

8.6 Residential Treatment Centers

Residential treatment centers have increased in popularity
during the past few decades. Based on the data in Table 8.1,
32% of all residential facilities for juveniles are residential
treatment centers. At their basic level, these centers provide
treatment services to juveniles in a residential environment.
For example, youth may attend school at the facility during
the day and group-based treatment (e.g., for substance
abuse or anger management) in the evenings or on
weekends. The focus of the facility is treatment rather than
punishment. The centers are intended to serve youth who
present with significant issues (e.g., behavioral or
emotional) that are not deemed severe enough to warrant
placement in a long-term secure correctional facility. In
theory, the facilities are designed to be short term to
stabilize and provide the youth with treatment.

As with all of the facilities and programs we have
discussed, the juveniles served at these facilities vary

greatly as well. According to Zelechoski et al. (2013), the majority of youth housed in these facilities tend to
have severe emotional and behavioral problems, complex histories of trauma and abuse, and significant issues
with regard to family, schools, and peers. Moreover, Preyde et al. (2011) found that half of the youth attending
residential treatment centers in their study did not live with their parents prior to their admission to the facility.
At the same time, painting these centers and the youth they serve with such a broad brush is difficult. Some
residential treatment centers may primarily admit high-need youth, whereas others may serve youth from less
severe backgrounds.

As a result, two critical issues or concerns emerge with regard to these centers. First, there are concerns that these
facilities could be mixing together high-risk youth with those who are lower risk. As a result, the centers could
act as “crime schools,” increasing the problems of the low-risk youth (Holman & Zeidenburg, 2013). The second
concern is whether these centers are sufficiently intensive. As we just discussed, these centers often serve youth
with significant behavioral problems and histories involving complex trauma and abuse. As such, the treatment
should be sufficiently intensive to address the youth’s needs. For example, studies suggest that treatment should
last 3–12 months depending on the youth’s needs (Lipsey, 2009). However, the length of stay at these facilities is
often relatively short and doesn’t appear to vary based on needs. Baglivio et al. (2018) found that when
residential centers addressed the youth’s needs and provided appropriate treatment dosage, outcomes improved
greatly.

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8.7 Long-Term Secure Correctional Facilities

Long-term secure correctional facilities are the closest parallel to adult prisons. The labels given to these
facilities vary by state. For example, North Carolina refers to its four secure institutions as youth development
centers, Ohio refers to its three secure institutions as juvenile correctional facilities, and California refers to its
three secure facilities as youth correctional facilities. By contrast, Rhode Island refers to its one secure residential
facility as a training school.

The Characteristics

According to Sickmund et al. (2017), just over 12,000 youth were committed to long-term secure facilities in
2015. The size of the facilities varies quite a bit, as illustrated in Figure 8.5. For example, 20% house between 21
and 50 youth, 50% hold between 51 and 150 youth, and 16% hold more than 200 youth.

Figure 8.5: Long-term secure facilities by size in 2015

Long-term secure facilities vary widely in size.

From “Table: Facility size by year of census,” in Easy access to the census of juveniles in residential placement:
1997–2015, by M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera, 2017, Retrieved from
h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?
ro w _ v a r = v 1 0 & c o l _ v a r = v 0 1 & d i s p l a y _ t y p e = c o l p & e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 1ro w _ v a r = v 1 0 & c o l _ v a r = v 0 1 & d i s p l a y _ t y p e = c o l p & e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 1
1 0 & v 1 2 8 = v 1 2 81 0 & v 1 2 8 = v 1 2 8 ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ? ( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p ?
ro w _ v a r = v 1 0 & c o l _ v a r = v 0 1 & d i s p l a y _ t y p e = c o l p & e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 & v 1 2 8 = v 1 2 8 )ro w _ v a r = v 1 0 & c o l _ v a r = v 0 1 & d i s p l a y _ t y p e = c o l p & e x p o r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 & v 1 2 8 = v 1 2 8 )

What are the demographic profiles of youth held at these facilities? Eighty-seven percent of those in custody are
boys, and, as shown in Figure 8.6, 43% are African American (Sickmund et al., 2017). Although the charges for
which youth were incarcerated vary (see Figure 8.7), the most frequently occurring offense type is person
offenses, which include robbery, aggravated assault, and sexual assault.

https://www.ojjdp.gov/ojstatbb/ezacjrp/asp/display.asp?row_var=v10&col_var=v01&display_type=colp&export_file=&printer_friendly=&v0110=v0110&v128=v128

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Figure 8.6: Percentage of youth sent
to long-term secure facilities by race,
2015

From “Table: Year of census by race,” in Easy access to the
census of juveniles in residential placement: 1997–2015, by
M. Sickmund, T. J. Sladky, W. Kang, and C. Puzzanchera,
2017, Retrieved from
h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / s e l e c t ih t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / s e l e c t i
o n . a s p ?o n . a s p ?
ro w _ v a r = v 0 1 & c o l _ v a r = v 0 3 & d i s p l a y _ t y p e = & e x p oro w _ v a r = v 0 1 & c o l _ v a r = v 0 3 & d i s p l a y _ t y p e = & e x p o
r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 & v 1 2 8 =r t _ fil e = & p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 & v 1 2 8 =
v 1 2 8v 1 2 8
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / s e l e c t i o n . a s p( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / s e l e c t i o n . a s p
??
ro w _ v a r = v 0 1 & c o l _ v a r = v 0 3 & d i s p l a y _ t y p e = & e x p o r t _ fil e =ro w _ v a r = v 0 1 & c o l _ v a r = v 0 3 & d i s p l a y _ t y p e = & e x p o r t _ fil e =
& p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 & v 1 2 8 = v 1 2 8 )& p r i n t e r _ f r i e n d l y = & v 0 11 0 = v 0 11 0 & v 1 2 8 = v 1 2 8 )

Figure 8.7: Percentage of youth sent
to secure facilities by offense type,
2015

Forty-eight percent of offenses were
associated with persons and 24% with
property, while only 4% of offenses were
associated with drugs.

https://www.ojjdp.gov/ojstatbb/ezacjrp/asp/selection.asp?row_var=v01&col_var=v03&display_type=&export_file=&printer_friendly=&v0110=v0110&v128=v128

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Detention center services such as education
Alan Spearman/The Commercial Appeal/Associated Press

From “Year of census by most serious offense general,” in
Easy access to the census of juveniles in residential
placement: 1997– 2015, by M. Sickmund, T. J. Sladky, W.
Kang, and C. Puzzanchera, 2017, Retrieved from
h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l ah t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a
y. a s py. a s p
( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p )( h t t p s : / / w w w. o j j d p . g o v / o j s t a t b b / e z a c j r p / a s p / d i s p l a y. a s p )

Does Confinement Work?

The use of punitive strategies for juveniles became popular in the 1980s and 1990s. This phenomenon was seen
in the rate of out-of-home placements for youth. For example, placements in residential facilities increased by
more than 40% during the 1990s (Snyder & Sickmund, 2006). Now, however, we are seeing a significant
reduction in the number of juveniles placed in long-term secure facilities. The decline in the number of juveniles
in custody is partly reflective of the reduction in arrests for juvenile delinquency. However, as mentioned in the
beginning of the chapter, it also is likely emanating from two additional sources: costs and effectiveness.

Costs
Institutionalizing juveniles is not a cost-efficient sanction.
In fact, the American Correctional Association estimates
that it costs as much as $88,000 per year to house a juvenile
in a high-security institution (although the figures vary by
state). This figure is particularly high compared to other
community-based sanctions. For example, one study found
that the average costs of community-based programs were
estimated to be close to $9,000 per year, compared to just
over $57,000 for a secure facility in Ohio (Lowenkamp &
Latessa, 2005).

The high cost of incarcerating juveniles comprises staffing

https://www.ojjdp.gov/ojstatbb/ezacjrp/asp/display.asp

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make long-term confinement an expensive
option compared to other community-based
sanctions.

Influences and Treatment

Running treatment in prison can be rewarding and
difficult.

1. Discuss the benefits of running treatment programs in
prison.

2. Identify the potential pitfalls and how they may be
overcome.

costs, the amount of money needed to run the institution
(e.g., heat, water, food), and costs to maintain services
(e.g., medical, mental health). Added services can increase
the cost dramatically. A study in California revealed that
incarcerating a juvenile with mental illness can increase the cost by as much as $18,800 per year (Cohen &
Pfeifer, 2008, p. 31).

Some of the costs of confinement can be justified on the grounds of public safety. In other words, if incarcerating
juveniles leads to a reduction in crime and makes neighborhoods safer, the costs might be worth it. However, the
issue of the effectiveness of long-term incarceration of juveniles is not as straightforward as it might seem.

Effectiveness
The effectiveness of confining youth is
complex and difficult to assess. If we take
a step back and examine this issue from a
philosophical perspective, we should ask
ourselves, What is the purpose of
confinement? For example, there are
typically said to be four primary goals of
confinement: retribution, deterrence,
incapacitation, and rehabilitation. Let’s
examine each in detail.

Retribution rests with the notion of
revenge for the harm a criminal has
inflicted on society. Retributive policies
have one intention: to punish. The
justification for punishment is not about
why youth commit crime or what social
circumstances should be changed in their
lives. Rather, the focus and the intent rest
with punishment for the youth’s
transgressions. Retribution as a goal of
incarceration isn’t necessarily related to
effectiveness. In other words, the
punishment is for punishment’s sake, not
to change behavior for the future.
However, if asked, most people would say
they hope the punishment produces a
long-term change in the incarcerated
youth. The idea that punishment should
produce future changes in youth is the
foundation for deterrence theory.

Deterrence theory asserts that punishment should reduce the future likelihood of crime. This can be
accomplished in two ways. First, punishment sends a message to juveniles that certain behavior is not acceptable,

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that a sanction will occur if they conduct themselves in a particular way. The sanction should teach youth that
there are consequences for behavior, and this consequence should reduce future criminal behavior. This
phenomenon is referred to as specific deterrence. Second, the punishment may have a wider effect on the
behavior of others who see that the youth was punished. You may hear judges or prosecutors say they want to
“send a message” to would-be criminals that the behavior in question will not be tolerated in the community. A
judge in that circumstance may sentence the youth to an institution in the hopes that doing so will make others
who may be thinking about committing a crime reconsider their actions. This is referred to as general deterrence.
During the get-tough period of the 1980s and 1990s, the philosophy of deterrence became increasingly popular.
This was seen in both the transfer of juveniles to the adult system (and thereby adult prisons) and the increase in
the use of confinement in juvenile institutions.

Incapacitation is a third goal that fits with the confinement of juveniles. The logic of incapacitation is that a
person who is confined cannot commit crime. Although this is not exactly accurate, given that juveniles can
commit crimes of violence or theft while confined, it does minimize crime in the community. It can be argued
that incarcerating juveniles during the years in which they are at higher risk for committing crime (e.g., 16–18
years of age) would reduce the crime rate.

Finally, a fourth goal, rehabilitation, maintains that providing treatment services for youth should be the guiding
philosophy for changing troubled behavior and reducing crime. Treatment or rehabilitation focus on the issues or
problems that propelled the youth into delinquency. Supporters would argue that if we can fix those issues or
problems, then we could expect the youth to refrain from committing crime again in the future. In other words, if
you fix the “cause,” you can fix the problem.

If we examine the impact or effectiveness of confinement for juveniles, we need to ask ourselves, Is the goal of
confinement to punish, to deter, to incapacitate, or to rehabilitate? If it is simply to punish for the sake of revenge
for the wrongdoing, then confinement could be argued to serve that purpose. If the purpose is to incapacitate to
reduce crime in the community while the youth is confined, one could argue that such a goal, at least on the
individual level, is likely realized. However, if the goal is either to deter or to rehabilitate, then effectiveness
becomes questionable.

Holman and Zeidenburg (2006) suggested that confinement of youth, particularly in long-term secure placement,
increases the risk of recidivism. They argued that the increase in recidivism can be attributed to a number of
things. Confinement has been found to diminish the mental health of those youth struggling with mental illness;
to label youth as “criminal,” thereby increasing their chances of identifying as criminals; to decrease their
chances to associate with positive peers who might help them get on the right path; and to reduce school
achievement, which further limits opportunity and further entrenches youth in the criminal justice system.

With regard to rehabilitation, studies suggest that if a prison
culture is one that supports treatment services, it can be
more effective. For example, most institutions provide
educational services for youth where they attend school for
a significant portion of the day. Moreover, many offer
group-based treatment services targeted at substance
addiction, life-skills development, or victim awareness.
Others provide vocational opportunities for youth. These
may include computer programming, woodworking,
agricultural activities, or auto repair. Although youth do not
leave the facility fully equipped for these careers, the
exposure may increase their interest in pursuing a

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Studies suggest that vocational programs
offered in long-term secure institutions may
increase a youth’s interest in studying the area
once released.

iStockphoto/Thinkstock certificate or degree in some area. Studies find that youth
who serve time in treatment-oriented facilities have better
attitudes toward the institution (Mancheck & Cullen, 2014).

The reality, however, is that long-term secure institutions
often prioritize confinement and security over
rehabilitation. Studies find that institutions focusing on

custody can inadvertently create the violence they are trying to prevent. For example, studies find that highly
punitive institutions that were coercive toward youth actually encouraged violence. A highly coercive
environment encourages youth to create a hierarchy within the institution in order to gain some sense of power
and control over their environment. Stronger youth then prey on weaker youth and treat those youth the same
way the guards are treating them (de Valk, Kuiper, van der Helm, Maas, & Stams, 2016; Feld, 1978).

Further illustrating this point is a recent study on prison rape in juvenile institutions. The Bureau of Justice
Statistics (BJS) conducted the study in response to legislation passed in 2003 called the Prison Rape Elimination
Act (PREA). The PREA legislation is designed to address the problem of sexual victimization in prison (for
juveniles and adults). The act has several provisions including the tracking of sexual victimization incidents in
prison. BJS now publishes annual statistics on the topic of sexual victimization among juveniles in correctional
institutions. According to the latest statistics, nearly 1,500 youth reported being sexually victimized while housed
in a facility. The rate of victimization increased between 2005 and 2012. The reason for this upward trend is
unclear.

The 2012 victimization study (U.S. Department of Justice, 2016) also included other insights into violence that
occurs within these facilities, including the following:

55% of the incidents involved youth-on-youth violence; 45% staff on youth.
Violence or threat of violence was used in nearly a quarter of youth on youth incidents.
64% of staff involved incidents were perpetrated by a female staff member.
State juvenile systems have higher rates of victimization than local or private juvenile facilities.

With such high rates of violence, the question shifts from is rehabilitation a goal of these facilities to can it be a
goal? Exacerbating the problem is the transfer of juveniles to the adult system.

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8.8 Juveniles in Adult Facilities

Once transferred to the adult criminal court system, juveniles can be sent to adult prisons to serve their period of
confinement. The trend peaked in the late 1990s when more than 5,000 persons under the age of 18 were housed
in adult prisons. According to the latest statistics, just under 1,000 juveniles are housed in adult prisons (Carson
& Mulako-Wangota, 2018).

Unfortunately, we see a racial disparity in terms of who is more likely to be sentenced to prison once transferred
to the adult court system. For example, an analysis of juveniles in adult custody published in 2000 found that “in
comparison with the adult prison population, a higher proportion of youth were black (55% of youthful inmates
versus 48% of adult inmates) and were convicted of a crime against persons (57% of youth versus 44% of adult
inmates)” (Austin, Johnson, & Gregoriou, 2000, p. 12).

How well do these juveniles fare in adult prisons? The research finds that they typically do not fare well
compared to juveniles kept in the juvenile justice system. For example, juveniles transferred to adult court fail
more often, more quickly, and in a variety of ways compared to those retained in the juvenile justice system
(Lambie & Randall, 2013). A study by Kuanliang, Sorensen, and Cunningham (2008) found that juveniles in
adult prisons had significantly higher rates of disciplinary infractions than adults in the same prison. And another
study found that these juveniles were more likely to be sexually assaulted when in adult prisons (Fagan &
Kupchik, 2011).

Given these findings, many states have revised their transfer laws for juveniles. A report issued by the Council of
State Governments notes that several states in particular are giving judges more discretion in allowing juveniles a
second chance:

In 2007, Virginia changed the “once an adult, always an adult” law. Previously, a one-time transfer of a
juvenile to adult court was enough to keep a juvenile in the adult system for all future proceedings, no
matter how minor the charge.
In 2008, a Colorado act allowed a juvenile charged with felony murder to serve in the juvenile justice
system. Virginia allowed a juvenile sentenced as an adult to gain earned sentence credits while serving
the juvenile portion of the sentence in a juvenile center rather than in an adult facility.
In 2008, a Maine law provided that juveniles under age 16 who receive adult prison sentences can begin
serving the sentence in a juvenile facility.
In 2009 and 2010, Nevada, Mississippi, and Utah left it to the juvenile court to determine whether
transfer to the adult court was necessary.
In 2012, a Colorado law barred “district attorneys from charging juveniles as adults for many low- and
mid-level felonies.” The act also raised from 14 to 16 the age at which young offenders may be charged
as adults for more serious crimes (Brown, 2012, p. 5).

The juveniles’ experiences while confined can influence how well they assimilate back into society. It would be
reasonable to expect that youth who experience coercive prison environments are more likely to do worse when
they return to the community. One study found this to be true among adult prisoners (Listwan, Sullivan, Agnew,
Cullen, & Colvin, 2013). Those individuals who experienced victimization in prison were more likely to return
to prison, a finding that runs counter to what deterrence theory would suggest.

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Despite reentry and aftercare programs, some
juvenile offenders return to detention centers
shortly after release.

Ken Tannenbaum/SuperStock

8.9 Preparing for Release

Approximately 100,000 youth reenter the community each year. Reentry is the label typically used to describe
the process youth go through when they return to the community from a period of confinement. The process
varies for each individual. Some youth may be returning to an alternative environment such as foster care; others
will return to the same environment they previously left.

Post-Release Challenges

Although the process of reentry is not new, there is
renewed attention surrounding reentry services for youth.
The attention is understandable if we consider that a study
of adults found that the vast majority of those reentering
the community failed within three years (Alper, Durose, &
Markman, 2018). According to one study of juveniles
detained in Wisconsin, 70% were arrested or returned to
secure detention within one year of release (Bezruki,
Varana, & Hill, 1999).

The question is, Why do so many juveniles struggle after
release? There are a number of possibilities. Some youth
may not be receiving adequate treatment services while
institutionalized. As we have discussed, treatment services
are often lacking in both short- and long-term facilities.
There are also concerns that confinement in a residential facility disrupts the key protective factors of school,
peers, and family relationships. For example, youth who are removed from traditional school settings when
placed in prison must be integrated back into the school system or, if they obtained a GED while
institutionalized, they must determine how to find their way into the workplace. Meaningful employment for
youth is difficult even in a healthy job market. As noted by Nellis and Wayman (2009), even when programs
exist in the institutions where youth are placed, “vocational programming designed to prepare young people for a
job upon release was not accompanied by any industry certification or associated with high-growth jobs in the
communities where the youth would be returning” (p. 18).

Over the past decade, however, there have been a number of reentry reforms in juvenile justice. For example, as
we’ve discussed, many states have chosen to reduce the number of commitments to long-term secure facilities.
This reluctance to incarcerate is a response to both the costs and the effectiveness of such a sanction. States have
had to be mindful of what services are needed in the community to ensure that youth are well served. One
example is Wraparound Milwaukee, a program that is a collaboration among mental health, juvenile justice,
child welfare systems, and educational systems. This program provides services to youth in the areas of
education, mental health, substance abuse treatment, and in-home therapy. Family is a key component of the
program as well. Similarly, as we discussed at the beginning of the chapter, Ohio’s Reentry Continuum has been
developed to guide that state’s practices.

The importance of treatment services as we prepare youth for release is not new. Research supports the idea that
aftercare services are needed to help youth transition into the community.

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Aftercare

Preparing youth for their release back into the community is one of the central tenets of aftercare. Aftercare
provides the client with services that focus on ongoing community support and treatment. Steve Geis (2003)
noted:

Two key components of the aftercare concept distinguish it from the traditional juvenile justice model.
First, offenders must receive both services and supervision. (Offenders in the traditional juvenile
justice system are generally sentenced to some type of supervision and are sometimes provided with
services.) Second, they must receive intensive intervention while they are incarcerated, during their
transition to the community, and when they are under community supervision. This second component
refines the concept of reintegrative services to include services that occur before release as well as after
release. (p. 1)

Aftercare should not simply be an occasional meeting with a probation officer. Instead, aftercare should focus on
the issues the juvenile faces. For some, that may mean an intensive intervention that includes multiple levels of
services given to both the youth and the family. For example, a youth who is returning to a nonsupportive,
chaotic family environment with exposure to drug and alcohol abuse will need a different level of service than a
youth who is returning to a stable family environment.

According to Nellis and Wayman (2009), reentry programs for youth should, at a minimum

Be located in the community where returning youth live
Be individualized to assist with developmental deficits
Concentrate heavily on ensuring school reenrollment, attendance, and success
Focus on permanent family/guardianship connections
Include access to mental health and substance abuse treatment
Recognize the diverse needs of returning youth
Include a structured workforce preparation and employment component
Include housing support and assistance for youth who cannot live with relatives and are transitioning to
adulthood

Although these components are important, the reality is that many state juvenile justice systems are fragmented
and not organized around meeting the needs of these youth in a systematic way. For example, most states have
both rural and urban areas. In urban areas, there are often more services available to youth simply due to the
needs of the population. Chicago will have more services available to youth than, say, a smaller town in Illinois.
Rural areas simply do not have the resources of larger towns or cities. So the aftercare experiences of youth
returning to a smaller town will differ from those of youth returning to a rural area. In effect, the quality of
aftercare is dependent on geography.

Pennsylvania has developed a comprehensive aftercare model based on the state’s Comprehensive Aftercare
Reform Initiative. The initiative led to the creation of 17 goals related to aftercare, including early assessment
and planning, multiagency collaboration, monitoring, school reintegration, and case management. Each probation
officer must develop a comprehensive plan that includes the school, family, and others who could act as
protective supports for the youth.

Intensive Aftercare Program

(IAP)

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Teens Transition to the Outside World

Teens re-entering the community from a period of
confinement are at great risk for recidivism.

Teens Transition
to Outside World
From Title:

On the Outside: Social Challenges for Teens Re…
(https://fod.infobase.com/PortalPlaylists.aspx?
wID=100753&xtid=37256)

© Infobase. All Rights Reserved. Length: 03:27

1. What other ways would you suggest kids cope with
stress at home and school?

2. Why is it important to match the type of activity to the
youth’s interests?

In another example, the OJJDP sponsored
the evaluation of a project referred to as
the Intensive Aftercare Program (IAP).
The IAP is designed to provide youth with
aftercare services that begin while they
are incarcerated and continue during
reentry into the community. Program staff
develop collaborations with agencies in
the community, and, while
institutionalized, youth receive services
designed to prepare them for the
transition. For example, vocational
programs offered while youth are
institutionalized could then be linked to
similar programs or even job
opportunities in the community.

The IAP model is a comprehensive
service-based approach that enables youth
to make a structured transition back into
the community rather than a transition that
is haphazard or based on things like
geography. To ensure the approach is
systematic and structured for each youth,
case management is key (Altschuler &
Armstrong, 1996). For example, staff
should not wait until the youth is about to
go back home to start thinking about
reentry. Instead, staff in these programs
should begin thinking about the youth’s
transition back to the community from the
beginning of the youth’s confinement.
This gives program staff time to develop a
plan for how youth will transition back,
where youth will go, and how to best
support youth and their families (Geis,
2003).

The approach is not simply about treatment services, although that is a significant component of the program. For
example, program staff also advocate for the importance of supervision services for youth. The probation or
parole officer is still seen as key to the youth. This may include home visits, drug testing, school monitoring, and
many of the surveillance activities discussed in Chapter 7. Initial reviews indicated that this approach was
successful; however, other studies suggest that it did not achieve reductions in recidivism (Wiebush, Wagner,
McNulty, Wang, & Le, 2005). Studies suggest that this approach often failed to engage families in meaningful
ways that could have led to long-term benefits (Abrams, Mizel, Nguyen, & Shlonsky, 2014).

https://fod.infobase.com/PortalPlaylists.aspx?wID=100753&xtid=37256

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Serious and Violent Offender Reentry Initiative (SVORI)
Another example of a structured approach to reintegration and aftercare is the Serious and Violent Offender
Reentry Initiative (SVORI). In 2003, the federal government launched the SVORI to address the needs of
violent adult and juvenile offenders reentering the community. According to Lattimore, MacDonald, Piquero,
Linster, and Visher (2004),

the goals of the initiative are to improve quality of life and self-sufficiency through employment,
housing, family and community involvement; improve health by addressing substance use (sobriety
and relapse prevention) and physical and mental health; reduce criminality through supervision and by
monitoring noncompliance, reoffending, rearrest, reconviction, and reincarceration; achieve system
change through multi-agency collaboration and case management strategies. (p. 2)

Ultimately, 69 agencies nationwide received over $100 million in funds to develop reentry programs. The 69
states agencies included 88 different programs. According to Lattimore and colleagues (2004), of those
programs, 35 targeted adults only, 34 targeted juveniles only, 2 targeted youthful offenders only, and 17 targeted
some combination of adults, juveniles, and youthful offenders. The SVORI programs for youth are similar to the
programs just noted. The delivery of services begins during the youth’s period of incarceration and continues
with the youth while in the community. The process was designed to be structured and to work with youth at all
levels (e.g., families, schools, peers, community).

So the question is, Did the SVORI programs fare better than the IAP programs? Unfortunately, the answer is no
if we look at the national data. Overall, the SVORI programs did not significantly reduce the recidivism rates of
youth participating in the program compared to youth who did not participate in the program. Among juvenile
clients, there was no difference in reported substance abuse or in criminal behavior. However, a few differences
between the two approaches were noted. For example, SVORI participants did slightly better with regard to
housing or employment; however, the programs were not considered a success due their lack of outcomes
(Lattimore & Visher, 2009).

Why did these programs fail to achieve the expected results? Implementing treatment for youth is difficult. In
Chapter 10 we will summarize these issues and identify promising or effective interventions for youth.

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Summary of Learning Objectives

Summarize the history behind the residential placement of youth.

The use of confinement for juvenile delinquency remains controversial.
The majority of states have substantially reduced the number of confined youth.

Define confinement and who is most likely to be sentenced to institutions for juveniles.

Juvenile facilities used for confinement are varied and complex. The terminology used to define juvenile
facilities varies so greatly that the terms residential or out-of- home placements are often used rather
than the term prison.
In 2016, over 45,000 juveniles under the age of 21 were held in juvenile residential facilities. This
represents a 53% decline since 2000.
Overall, 15% of these youth are girls.

Explain the different types of short-term residential placements for youth.

Short-term residential facilities include detention centers, reception/diagnostic centers, and youth
shelters.
In the majority of cases, these placements provide temporary housing for youth prior to adjudication.

Describe the advantages and disadvantages of group homes.

Group homes are smaller than other residential facilities and can provide a variety of treatment services
to youth.
Group homes that rely simply on supervision without treatment services tend to be ineffective.

Explain the degree of effectiveness of wilderness camps.

Wilderness programs are designed to increase self-efficacy of the youth by exposing them to challenging
situations.
Overall, the camps are found to be ineffective as they do not focus on changing the problems youth face
in their communities.

Identify the different types of short-term residential placements for youth.

Residential treatment centers are the newest type of residential facilities that typically serve youth with
complex needs who otherwise would have been sent to long-term secure correctional facilities.
Though more effective than group homes and wilderness camps, residential treatment centers show
mixed results. It is more effective to treat youth in the community than in a residential environment.

Summarize the issues associated with long-term secure correctional facilities.

Long-term secure correctional facilities serve youth deemed a risk to the community. The rate of youth
placed in custody has declined since the early 2000s.
Long-term secure facilities are criticized for their high cost and lack of effectiveness (i.e., their failure to
reduce recidivism). Studies suggest these facilities fail to rehabilitate or deter youth from future criminal
acts.

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Describe the risks involved with confining juveniles in adult facilities.

Transferring youth to adult court became popular in the late 1980s and 1990s. By the late 1990s, more
than 5,000 persons under the age of 18 were housed in adult prisons. Today fewer than 1,000 are housed
in adult prisons.
Studies suggest that youth transferred to adult facilities have worse outcomes than those who remain in
the juvenile justice system.

Identify the components of successfully helping juveniles reintegrate into society after release.

Release or reentry back to the community is an important issue for juvenile delinquents.
Aftercare services are crucial for youth returning home from a period of confinement.
There are several notable programs; however, studies suggest aftercare programs struggle to provide the
services required to address juveniles’ needs.

Critical Thinking Questions
1. Do you like the idea of wilderness camps or ranches? Why or why not? Are you dissuaded by mixed

findings regarding their effectiveness?
2. What do you see as the biggest problems with long-term secure facilities for juveniles? Do you support

reducing their use or increasing it? Explain your answer.
3. Do you agree with the states that have begun revising their transfer laws for juveniles to make them less

stringent? Or do you believe we should continue to transfer juveniles to the adult system? Explain your
answer.

4. Why do you think that juveniles struggle as they return back to the community from a period of
incarceration? What should we do about it, particularly given the mixed findings regarding the IAP and
SVORI programs?

Key Terms
Click on each key term to see the definition.

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Short-term facilities used to confine youth before the intake or adjudication phase.

deterrence theory
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The theory asserting that punishment should reduce the future likelihood of crime through specific deterrence,
which sends a message to juveniles that certain behavior is not acceptable and so not to repeat criminal behavior,
and general deterrence, which is intended to send a message to other would-be criminals.

Disproportionate Minority Contact (DMC)

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An initiative designed to reduce the number of minorities who come in contact with the juvenile court system.

experiential learning

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A learning approach built on the idea that self-efficacy is attained through direct experiences.

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Either short- or long-term facilities that serve a variety of youth in the juvenile justice system. Group homes
typically provide supervision and services in a home-like setting.

incapacitation
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The confinement of individuals who commit criminal acts.

Intensive Aftercare Program

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A program designed to provide aftercare services to youth beginning while they are incarcerated and continuing
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A term frequently used for juvenile facilities in place of terms such as prison.

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The number of juveniles in custody per 100,000 youth.

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Facilities that typically house youth for short periods while correctional officials assess the juveniles’ needs in
order to determine the best placement.

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A goal of confinement according to which providing treatment services, not punishment, for youth should be the
guiding philosophy for changing troubled behavior and reducing

crime.

retribution
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A goal of confinement according to which revenge is enacted for the harm a criminal has inflicted on society; the
primary intention of retributive policies is to punish.

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A structured approach to reintegration and aftercare launched to address the needs of violent adult and juvenile
offenders reentering the community.

youth shelters
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Facilities that provide short-term placement for youth who cannot be immediately returned to their families.

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A popular program most well known for its wilderness camps.

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9Special Populations

Cultura Limited/SuperStock

Learning Objectives
After studying this chapter, you should be able to accomplish
the following objectives:

Describe the issues early starters face in their

everyday lives, the characteristics of persistently

disruptive children, and the methods used to prevent

and treat early delinquent behavior.

Explain the complexity of defining gangs and gang

behavior and the relationship between gangs and

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In the summer of 1981, 6-year-old Adam Walsh was abducted from a Hollywood, Florida, shopping mall. His
mother allowed Adam to play video games near the front entrance while she shopped for lamps. She indicated
she was gone for only 10 minutes. According to the store security guard, he found Adam and several other boys
fighting over the video games and escorted all of them out of the store. It is believed that Adam was left alone
outside the store, at which point he was abducted.

Adam’s decapitated head was found approximately two weeks later; however, the police never recovered his
body. The national manhunt remained a cold case for years until, in 2008, officials declared that a previous
suspect of the murder was the killer. That man had died in prison 12 years earlier and was never officially tried
for Adam’s abduction and murder.

Adam’s father, John Walsh, is well known around the world as the man behind the television show America’s
Most Wanted. He is actively involved in missing children cases and advocating for harsher laws surrounding
child molesters. Some 25 years later, in 2006, President George W. Bush signed into law the Adam Walsh Child
Protection and Safety Act. The law, passed with bipartisan support, requires more stringent sex-offender
registration requirements. Although there was no evidence that Adam Walsh was sexually assaulted, given that
his body was never found, the man accused of his murder had a history of juvenile delinquency and sex
offending. The legislation was seen as a positive step toward holding sex offenders accountable. That
accountability is represented in a three-tier notification system that requires serious sex offenders to be listed on
a national registry for life.

Sex-offender registration laws are very popular with the public. The popularity of these laws is understandable—
people feel they have the right to know if a sex offender is living next door or in their neighborhood. One issue
we will examine in this chapter is whether the situ ation changes when a juvenile is involved. As discussed in
previous chapters, many serious adult offenders begin their criminal careers as juveniles. Yet we know that not
all juvenile sex offenders become adult offenders. In fact, juvenile sex offenders are more complex than the label
suggests.

crime.

Analyze the complexity surrounding the labeling of

juvenile sex offenders.

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9.1 Introduction

We often talk in terms of the “typical” juvenile delinquent, but certain juvenile delinquents are deserving of
special attention. These special populations present particular challenges for the juvenile justice system and, to
an extent, the communities in which they reside. For example, some juvenile delinquents are more likely to be
involved in criminal behavior at a young age, and they are more likely to continue their criminal involvement as
adults. Intervening in the lives of these juveniles is particularly important. Moreover, there are certain types of
juvenile delinquents whose behavior is more violent and detrimental to their families, schools, and communities.
These populations present special issues and concerns for the juvenile justice system, both from a policy
standpoint and from a rehabilitation standpoint.

The word special may seem a misnomer in this circumstance as it is typically used in a positive context. In this
chapter, however, we use special to refer to those who depart from typical patterns of criminal behavior among
adolescents. The three types of juvenile delinquents that most significantly depart from the norm are the very
young delinquents often called early starters, gang-involved youth, and juvenile sex offenders.

What is interesting about each of these types of delinquents is that although they share similarities they also are
very different. In particular, juvenile sex offenders are a heterogeneous group, meaning that all juvenile sex
offenders do not look alike, act alike, or in most circumstances have the same backgrounds. Understanding the
complex legal and social issues at stake with these three special populations is also important.

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According to current trends, young offenders
are more likely to engage in substance abuse at
an earlier age than in previous years.

BananaStock/Thinkstock

9.2 Early Starters

What does the label early starter mean? Although the age at which we label a juvenile an “early starter” is
somewhat debatable, the label is typically reserved for those who begin committing crimes before the age of 14.
The number of juveniles who commit crimes before that age is relatively small. In fact, most studies find that
early starters represent merely 5% to 7% of the juvenile delinquents arrested in any given year (Loeber &
Farrington, 2000). Terrie Moffitt (1993) categorized delinquents into two groups: adolescent limited offenders
and antisocial persistent offenders. Both groups can include early starters. Adolescent limited offenders typically
stop their delinquent behavior by the end of adolescence. By contrast, antisocial persistent offenders continue
their criminal careers into adulthood. They are also more likely to be early starters. In fact, studies find that early
starters are two to three times more likely than juveniles who start offending at a later age to become chronic
adult offenders (Loeber & Farrington, 2000).

Issues Faced by Early Starters

As youth, antisocial persistent offenders often encountered
cumulative disadvantages that exacerbated their problems.
Once these youth are off on the wrong foot, the cycle
becomes difficult to break. They are more likely to have
neurological difficulties that include impulsivity,
hyperactivity, and poor verbal and problem-solving skills
(Moffitt, 1993). Understanding how and why these
juveniles begin their delinquent careers is important for
managing and intervening with this population.

These early starters can be a drain on the juvenile justice
system. They are more likely than other juvenile offenders
to commit crime more frequently. They are also more likely
to escalate their criminal behavior and become increasingly
violent. As a result, the Office of Juvenile Justice and
Delinquency Prevention (OJJDP) formed a study group. The group, referred to as the Study Group on Very
Young Offenders, was comprised of 39 experts on the topic of child delinquency. The study group published a
number of reports that can be found at OJJDP.gov (https://www.ojjdp.gov) under the title Child Delinquency
Series. They classified young offenders as those aged 7 to 12. There are several interesting points to highlight:

Juveniles whose first referral to court for a delinquency offense had occurred before age 13 were far
more likely to become chronic offenders—that is, to have had at least four referrals to juvenile court—
than juveniles whose first referral had occurred when they were older.
Early starters, particularly boys, are at risk for becoming teenage parents.
Early starters are more likely to experience complex trauma, emanating from both their families and
communities.
Early starters are at higher risk for mental health issues such as depression and suicide.
Early starters are more likely to experience trouble in school and have higher truancy and dropout rates.
Early starters are more likely to begin using substances at an earlier age (Loeber & Farrington, 2001).

If early starters’ offending persists into adulthood, they are less likely to desist or stop committing crime. DeLisi

https://www.ojjdp.gov/

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and Piquero (2011) argue that as delinquents persist into their 20s and beyond, they become increasingly invested
in their criminal behavior, reducing their opportunities for legitimate work, relationships, and connections to the
community. Given the potential for these youth to become adult offenders, many argue that we should intervene
in their lives in order to prevent this destructive set of events from happening.

Prevention and Treatment

The OJJDP study group concluded that interventions focused on preventing child delinquency would exhibit the
greatest impact on crime (Loeber, Farrington, & Petechuk, 2003). These efforts should be focused in particular
on identifying and intervening with persistently disruptive children. Persistently disruptive children are those
who exhibit a pattern of negative behaviors and are defiant, disobedient, and hostile for a period of at least six
months. These disruptive youth often experience a number of problems in their lives. For example, they are more
likely to have trouble at home, be diagnosed with attention-deficit/hyperactivity disorder (ADHD), be victims of
child abuse, and do poorly in school (Manuzza, Klein, Bessler, & Maloy, 1993; Moffitt, 1990).

The logic here is that children involved in the juvenile justice system often exhibit disruptive behavior long
before their first official contact with the formal system (e.g., an arrest). Although not all of these youth will
become delinquent youth, they are more likely (some studies find about 25%–50% of these youth) to become
formally involved in the juvenile justice system (Loeber & Farrington, 2000). Identifying the youth who are
exhibiting disruptive behavior before they come into contact with the system is the best way to prevent the cycle
before it happens.

The family is an important factor for these youth. Family abuse, criminal behavior among family members (e.g.,
parents and siblings), lack of supervision, lack of closeness, and low socioeconomic status can all contribute to
the youth’s delinquency. Similarly, a study by Romeo Vitelli (1997) found early starters are more likely to have a
history of exposure to violence in childhood. A study by Alltucker, Bullis, Close, and Yovanoff (2006) found that
children who were in foster care were four times more likely to be early starters, and children with a criminal
family member were twice as likely to be early starters. Loeber and Farrington (2000) argue that serious
delinquents often begin committing delinquent acts in the home and then branch out into their schools and
community.

More recently researchers are exploring the importance of trauma on early starters. Those who experience
traumatic events are significantly more likely to experience psychological problems and involvement in the
juvenile justice system. Studies suggest that youth involved with the juvenile justice system are highly likely to
have a history of trauma exposure. In fact, a study of juvenile detainees in Cook County, Illinois, found that
92.5% of the youth sampled in the facility had experienced at least one traumatic event or experience (Abrams et
al., 2004). Another study, conducted by Kerig and colleagues (2009), found that 92.3% of their juvenile detention
sample had exposure to at least one traumatic event. Trauma-exposed youth are at risk for posttraumatic stress
disorder (PTSD), major depressive disorder, and substance abuse (Kilpatrick et al., 2000). Researchers have
begun exploring the impact of traumatic events in a more systematic way, using the Adverse Childhood
Experiences (ACE) scale, which we detail in the accompanying Spotlight.

Spotlight: The Adverse Childhood Experiences Scale

The Adverse Childhood Experiences (ACE) scale measures various forms of abuse and neglect (Centers

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for Disease Control and Prevention, 2016), which are outlined below. The specific items refer to the
respondent’s first 18 years of life.

Abuse

Emotional abuse: A parent, stepparent, or adult living in your home swore at you, insulted you,
put you down, or acted in a way that made you afraid that you might be physically hurt.
Physical abuse: A parent, stepparent, or adult living in your home pushed, grabbed, slapped,
threw something at you, or hit you so hard that you had marks or were injured.
Sexual abuse: An adult, relative, family friend, or stranger who was at least 5 years older than
you ever touched or fondled your body in a sexual way, made you touch his/ her body in a sexual
way, attempted to have any type of sexual intercourse with you.

Household Challenges

Mother treated violently: Your mother or stepmother was pushed, grabbed, slapped, had
something thrown at her, kicked, bitten, hit with a fist, hit with something hard, repeatedly hit for
over at least a few minutes, or ever threatened or hurt by a knife or gun by your father (or
stepfather) or mother’s boyfriend.
Household substance abuse: A household member was a problem drinker or alcoholic or a
household member used street drugs.
Mental illness in household: A household member was depressed or mentally ill or a household
member attempted suicide.
Parental separation or divorce: Your parents were ever separated or divorced.
Criminal household member: A household member went to prison.

Neglect1

Emotional neglect: Someone in your family helped you feel important or special, you felt loved,
people in your family looked out for each other and felt close to each other, and your family was
a source of strength and support.2
Physical neglect: There was someone to take care of you, protect you, and take you to the
doctor if you needed it2, you didn’t have enough to eat, your parents were too drunk or too high
to take care of you, and you had to wear dirty clothes.

Each experience of a traumatic event is considered “1” point. Items such as emotional neglect (i.e., those
without someone to help them feel important) are reverse scored. The original study conducted by the
CDC found that more than half of people experienced at least one traumatic childhood event. An ACE
score of 4 or more indicates an increased risk for depression, suicide, alcohol disorders, and other health-
related problems (Felitti et al., 1998). Researchers found that early starters were significantly more likely
than other juvenile offenders to have higher ACE scores and were more likely to persist offending into
adulthood (Baglivio, Wolff, Piquero, & Epps, 2015).

More information about the ACE scale can be found at:
https://www.cdc.gov/violenceprevention/acestudy/index.html
(https://www.cdc.gov/violenceprevention/acestudy/index.html)
1Collected during Wave 2 only.

https://www.cdc.gov/violenceprevention/acestudy/index.html

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Early disruptive
behaviors, such as
defiance, disobedience,
and hostility, are
considered indicators of
future delinquency.

Hemera/Thinkstock

2Items were reverse-scored to reflect the framing of the question.

Although there is little question that intervening with these youth is important, the
remaining problem is how and when to intervene. Intervention is more
complicated than it might at first seem. A young bully on the playground might be
tomorrow’s serious and persistent juvenile delinquent, but some aggressive
behavior among preschoolers and early elementary schoolers is normal as kids test
out their boundaries and relationships with others. When does aggressiveness
among young children go beyond the norm?

Experts suggest there are some early warning signs. For example, kids who are
more aggressive than is considered normal for their similarly aged peers may be a
concern. Others find that juveniles who show conduct problems in preschool are
more likely to eventually engage in property and violent crime (Loeber, 1988).
They also note that kids who are fire setters or show cruelty to animals are a
particularly high-risk group (Putnam & Kirkpatrick, 2005). And based on our
preceding discussion, youth who experience difficulties within the family and
school or have experienced trauma, foster care exposure, and abuse are at greater
risk for becoming chronic delinquents.

However, the question remains, How and when do we intervene? Who will assess
these early warning signs? Do we place the responsibility on the teacher to
identify and intervene? Should all children be required to answer a survey in
school to help identify troubled youth? Should schools be given the legal authority
to do so? Or should these youth only be identified once they become particularly

burdensome to the schools? One such intervention, First Steps to Success, argues that the schools should
intervene with youth as early as kindergarten (see the accompanying Spotlight).

Spotlight: First Steps to Success

Concerned with the prevention of juvenile delinquency, the OJJDP formed the Study Group on Very
Young Offenders. The study group was charged with identifying the needs and issues confronting these
youth with the ultimate goal of developing prevention and intervention techniques. Intervention
techniques are important for the youth, their communities, and ultimately for the juvenile justice system
(Walker et al., 1998).

Emerging from this study group was the First Steps to Success program, which is designed for youth 5 to
6 years old to prevent problems in school. The intervention targets youth whose teachers rate them as
troublesome. The ratings include some of the following behaviors:

Aggression
Oppositional defiant behavior
Severe fits of temper
Victimization of others

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The intervention calls for a three-pronged approach for identifying and managing early juvenile
delinquents:

Universal screening of all kindergartners
School-based intervention involving the teachers, child, and parents
Parent training and involvement

The program offers a variety of services:

Skill building with teachers to train them how to manage disruptive behavior
Awarding points to children for good behavior
Visual cues (green versus red cards) used by teachers to help youth identify good versus poor
behaviors
Rewards for youth who maintain good behavior
Written daily reports for parents who are instructed to encourage and reward behavior at home

A study conducted by Hill Walker and colleagues (1998) found that kids who participated in the program
were rated by teachers as more adaptive and less aggressive in school.

Interventions for these youth need to accommodate the fact that youth experience different types of problems at
different times in their lives. For example, for very young children the family environment is the most important.
As youth age, peers begin to replace parents as the most important socializing agent. Treatment and prevention
efforts must account for developmental issues or milestones in order to be responsive to the youth’s needs. Some
of the interventions geared toward prevention should be directed to very young children, which can mean as
early as the preschool or even infant years. A promising intervention is a nurse’s program that works with at-risk
mothers during the prenatal and postnatal stages. Others programs focus on peers, schools, parents, and
communities in an effort to intervene in the lives of at-risk youth.

Many of the youth in the other two at-risk populations addressed in this chapter—gang members and juvenile sex
offenders—could also be early starters.

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Gang Realities in Our World

The GROW unit at Pendleton Maximum Security Juvenile
Prison houses 24 gang members. The program attempts to
remove individuals from their gangs. A gang member
reflects on his environment outside prison and becoming
smarter after incarceration. GROW director Eric
Courtney discusses identifying gang members in the
system.

Gang Realities
in Our World
From Title:

Counseling and Psychology: Gang Counseling in …
(https://fod.infobase.com/PortalPlaylists.aspx?
wID=100753&xtid=52377)

© Infobase. All Rights Reserved. Length: 03:15

1. Why do you think housing gang members together
positively affects their behavoir?

2. Should we focus on the gang members’ past behavior,
or on life after gang involvement?

9.3 Juvenile Gangs

During the early 1900s, cities began to
experience increased crime rates as
populations migrated to urban centers for
better job opportunities. The increase in
crime rates resulted in part from the
emergence of gangs. This makes sense in
the context of migration. Early gangs
were often comprised of ethnic or racial
minority group members (e.g., Italian,
Irish, Russian) who came to the United
States as outsiders in search of a better life
(Curry & Decker, 2003).

The next cycle in the growth of criminal
gangs occurred in the 1960s (Klein,
1995b). During this time, African
American and Latino gangs increased in
prevalence. David Curry and Scott Decker
(2003) noted that these newer youth gangs
were different from those that developed
in the early 1900s. For example, they
argued the following:

These new gangs were more
extensively involved in criminal
activity, especially violence. The
availability of guns and automobiles
gave these gangs more firepower and
the mobility to interact with and fight
gangs in neighborhoods across a city.
The more extensive involvement in
crime, in turn, led to increased
convictions and prison time. As a
consequence, the prison became an
important site for the growth and
perpetuation of gangs. In Illinois and California in particular, the prison became an important site for
recruiting gang members and strengthening gangs. (p. 15)

The complex relationship between prison and community gangs is a significant issue for the police as they try to
reduce gang membership.

What Is a Gang?

The question “What is a gang?” seems like a reasonable question that should have a reasonably simple answer.

https://fod.infobase.com/PortalPlaylists.aspx?wID=100753&xtid=52377

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Gangs often claim territory by vandalizing
public areas with graffiti.

Kevork Djansezian/Associated Press

However, this is not the case. The U.S. Department of Justice officially defines a gang as an association of three
or more individuals who adopt a collective identity and engage in criminal activity with the intention of
preserving the power or economic resources of the group (Federal Bureau of Investigation, 2015). But the
complexity has more to do with how to define small or disorganized gangs than with identifying large organized
gangs.

For example, several large, well-known gangs operate in
the United States. Those gangs include the Aryan
Brotherhood, Bloods, Crips, Latin Kings, Black Gangster
Disciples, MS-13, and the Mexican Mafia. These gangs are
organized and by many estimates include thousands of
members. For example, the Crips are thought to have over
30,000 members while the Latin Kings boast between
20,000 and 30,000 members spread throughout the country
(National Gang Intelligence Center, 2011). But what about
other gangs that are less organized or have fewer members?
Some police departments even consider a group of two or
more individuals a gang. But we know that two or even
three teenagers picked up for vandalizing school property is
not an example of a gang in the traditional sense.

Curry and Decker (2003) argue instead that the criteria for
defining gang members must include several elements:

Symbols: often include colors, tattoos, clothing
Communication: handshakes/signs, graffiti
Permanence: gang exists over a period of time
Turf: territory, can be as small as a street to as large as an entire neighborhood
Crime: involvement in illegal behavior

Curry and Decker make the point that an element of family, of a form of kinship and support, is also a key
feature in gangs. The idea of gangs representing family is common to many gang members they interviewed:

“Well, in my words, a gang ain’t nothing but people come together to do crime and make money and be
a family to each other… It’s a second family. They are like your brothers. They will help you out when
you need help. Whenever you mess up they will take care of you too.” (Curry & Decker, 2003, p. 11).

This touches on the lack of support and/or trauma that many juvenile delinquents experience at early ages, which
further complicates the formation of gangs and the related criminal behavior that ensues.

Gang Prevalence in the United States

Several attempts have been made to measure gang involvement, from interviewing gang members to asking
police departments to quantify how many gang members they have arrested. The most comprehensive source of
information on the extent of youth gangs in the United States comes from the OJJDP’s National Youth Gang
Survey (NYGS). The NYGS began in 1995 and surveys police departments about gang activity in their areas.
Survey results were collected annually for over a decade, covering on average nearly 2,300 police departments.

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The most recent survey was conducted in 2012.

Survey results indicate that there were around 30,000 gangs in the United States in 2012, with just over 850,000
members. Not surprisingly, gangs are more prevalent in larger urban areas than in suburban areas (more on that
later), and according to the 2011 NYGS, 46.2% of all documented gang members are Hispanic/Latino, 35.3% are
black or African American/black, and 11.5% are white (National Gang Center, n.d.).

Girl Gangs

Girl gangs and girl gang members represent an interesting and generally misunderstood subset of gang activity
(Sutton, 2017). According to the NYGS, gangs are comprised primarily of male members. In fact, in 2012, law
enforcement agencies reported that less than 10% of known gang members were girls. However, they note that
gangs with girl members (i.e., coed gangs) are much more common than girl-only gangs. Other studies find that
the percentages of female and male gang members may not be so different. In fact, Esbensen, Brick, Melde,
Tusinski, and Taylor (2008) found that nearly identical percentages of boys and girls self-reported gang
involvement.

The complexity surrounding the prevalence of girl gangs versus coed gangs led Walter Miller (1975) to create
three categories to describe girl gangs: (a) girl-only gang affiliated with a male gang, (b) girl-only gang with no
affiliation, and (c) coed gang. Miller argued that the girl-only gangs with no affiliation were nearly nonexistent.
More recent research finds that girl-only gangs are more prevalent than when Miller conducted his studies;
however, the prevalence of all-girl gangs is still lower than those considered coed or mixed gender (Peterson,
Miller, & Esbensen, 2001).

The most controversial aspect of girl gang members is the exact role they play in the gang. The OJJDP sponsored
a series of publications on youth gangs. A publication by Joan Moore and John Hagedorn (2001), titled Female
Gangs: A Focus on Research, noted that “most early reports focused on whether female gangs were ‘real’ gangs
or merely satellites of male groups” (p. 1). Instead of seeing girl gang members as equal to their male
counterparts, many saw girl gang members as simply sex toys or tomboys who acted as drug mules or weapon
carriers for their male members.

Recent research suggests that female gang members are more likely to have family members in a gang (Miller,
2002). However, the exact role they play is difficult to disentangle. Some studies suggest that girl gang members
are subordinate to male members, and others suggest that this finding may vary by race or ethnicity. For example,
some research suggests that Latina gang members may take a more subordinate role than is the case for girls in
African American gangs (Miller & Brunson, 2000). Other studies suggest girl gang members are fully integrated
and play an equal role. For example, male members may commit more delinquency, but researchers found that
both genders engaged in a range of delinquent acts (Melde & Esbensen, 2013).

Research examining the pathways that lead boys and girls into gangs has found both similarities and differences
between the two populations. In their bulletin, Moore and Hagedorn (2001) suggest a number of reasons girl
members choose the gang life. For example, economic poverty is a risk factor for both girls and boys, meaning
that those who come from impoverished backgrounds are more likely to seek out gangs, either for economic
survival or as a second family while parents are unavailable. In terms of differences, some research suggests that
the incidence of past sex abuse and trauma is significantly higher among female gang members than among male
gang members. Moreover, they may experience continued sexual abuse from other gang members after joining.
Regardless of these differences, however, additional studies are needed to examine these issues.

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As mentioned previously, one of the key features of gangs is their involvement in criminal activity (otherwise,
we could consider the Boy Scouts a gang). The criminal element is important for a variety of reasons, most
notably to help organize prevention and detection techniques to reduce gang membership.

Gang-Related Crime

If we examine the latest statistics, we see that gang members are responsible for a disproportionate amount of the
violent crime committed each year. In other words, if we look just at the raw numbers, juveniles affiliated with a
gang commit more violent crime than juveniles not affiliated with a gang. In fact, research suggests that gang
members commit between 68% and 85% of the serious violent crime committed by juveniles (Thornberry, 1998;
Thornberry, Huizinga, & Loeber, 2004). The NYGS (National Gang Center, 2012) makes the following
estimation:

In a typical year in the so-called “gang capitals” of Chicago and Los Angeles, around half of all
homicides are gang-related; these two cities alone accounted for one in five gang homicides recorded
in the NYGS from 2006 to 2010.

Moreover, the National Gang Threat Assessment survey results from 2011 indicate that up to 48% of the violent
crime committed in the United States each year is attributable to gangs, and that percentage is even higher in
some cities (National Gang Intelligence Center, 2011).

Even though we know that gang members contribute disproportionately to the crime rate, the other issue to
examine is the extent to which the criminal behavior is gang related or simply committed by an individual who is
affiliated with a gang but whose criminal behavior is unrelated to the gang. For example, if a youth steals from a
convenience store on his or her own accord, is arrested by the police for the crime, and is a known gang member,
the police may record this as a gang-related crime. Disentangling this question is nearly impossible given the
lack of data on the issue. For example, the NYGS reveals that other than for homicide and graffiti, most police
departments do not record whether a crime was gang involved.

One of the more interesting issues involves determining the features of gangs that seem to drive or increase the
likelihood of criminal behavior. For example, if we are to believe the previously discussed trends—gang
members are more likely to commit crime, particularly violent crime—then what are the driving forces behind
these statistics? It turns out that several prominent features may be influencing these rates.

First, some observers argue for a “birds of a feather flock together” perspective on gang membership. That is,
like-minded, violence-prone youth typically seek out one another and reinforce one another’s violent or
aggressive behavior (Gravel, Allison, West-Fagan, McBride, & Tita, 2018; Sanchez-Jankowski, 1991). Second, a
popular theory about gang involvement and crime, social contagion theory, argues that how people act in
groups may differ from how they would act independently. In particular, this theory argues that the members of
the group can influence collective behavior. Group members may feel anonymous within the group, leading them
to diffuse their own degree of responsibility for the behavior. Within this context, people become more
susceptible to influences by other members of the group (Nye, 1975; Papachristos, Braga, Piza, & Grossman,
2015). Third, given the prevalence of gangs in particular areas, we could surmise that environment plays a role.
Social disorganization theory provides an understanding of how cities can create crime through the lack of
opportunities for successful advancement into the labor market and a lack of cohesion among community
members. A final explanation might be that all of these factors work together to create an environment more
hospitable to gang violence. In fact, Decker and Van Winkle (1996) proposed that we examine how individual,

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Gang membership puts youth at risk of
becoming chronic offenders.

Stock Connection/SuperStock

group, and community factors interact to predict involvement in gang delinquency.

Gang members are often involved with drug-related crime. Some research finds that gangs can be very organized
in their approach to trafficking drugs, whereas other research finds that most gang members use or distribute
drugs on their own accord without involvement from the gang as a formal organization. For example, some
researchers argue that gangs can be highly organized and have formal leadership structures that facilitate highly
successful drug-trafficking rings (Padilla, 1992; Sanchez-Jankowski, 1991; Skolnick, 1990). In fact, the
organization acts much like a corporation with profits from the drug sales recycled back into the organization.
Within these gangs are leaders who must orchestrate a fairly complex system of trafficking in drugs, which
includes drug mules or carriers. Each of these members must be kept in check in order for the organization to run
smoothly.

Others argue that most gangs are disorganized and do not have the leadership structure in place that is noted in
these studies (Howell & Griffiths, 2018). They argue that most gangs are loosely organized and have many
transient members who often do not have the same goals. In fact, John Hagedorn (1988) interviewed gang
members in Milwaukee and argued that the gangs he observed were not well organized, did not have a system in
place to control their members, and were not profitable.

Consequences of Gang Membership

Let’s consider the possibility that many of these gang
members could be early starters as well. These individuals
may have been involved in early disruptive behavior that
has become more serious. They have often accumulated a
number of risk factors that put them at greater risk for
continuing their criminal careers. Researchers using meta-
analysis techniques to study risk factors for crime are able
to identify which risk factors are most important. For
example, Gendreau, Andrews, Goggin, and Chanteloupe
(1992) found that the most common risk factors they
surveyed included lower social-class origin (poverty),
personal distress/psychopathology, educational/vocational
achievement, parental/family factors,

temperament/personality, antisocial attitudes, and antisocial associates. The top five risk factors in this list are
school, family, personality, attitudes, and peers. Gang members often have trouble with each of these areas.

The gang can place youth on a negative trajectory that can hamper future opportunities for college, employment,
marriage, and so on. As such, these individuals are at risk for becoming chronic offenders who will continue
their criminal behavior well into adulthood. The concerns regarding youth gang involvement in crime and the
potential for these offenders to become chronic and persistent criminals has led to a number of anti-gang
programs and initiatives. In the following section, we will highlight a number of promising programs.

Gang Prevention: Programs and Initiatives

In Chapter 5, on policing, we discussed two gang prevention programs: the Gang Resistance Education and
Training (G.R.E.A.T) program and Boston’s Operation Ceasefire. Several other types of programs are designed

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to either reduce gangs or prevent gang violence.

First, the Administration on Children, Youth, and Families established the Youth Gang Drug Prevention
Program in the late 1980s. The program is designed to provide funding for communities to develop coordinated
efforts to reduce gang problems in their areas. The funding provided to agencies led to the development of a
range of programs including peer support, education, mentoring, crisis intervention, and recreation programs.
Unfortunately, evaluations of the programs found that they were ineffective at reducing gang involvement
(Cohen, Williams, Bekelman, & Crosse, 1995).

In the mid-1990s, the OJJDP developed another program, called the Comprehensive Gang Model. (Visit
http://www.nationalgangcenter.gov/Comprehensive-Gang-Model/About
(https://www.nationalgangcenter.gov/Comprehensive-Gang-Model/About) to learn more.) The model, originally
implemented in Chicago, is now operating in other states as well. It includes targeting five specific areas to
reduce and prevent gang involvement and gang-related crime:

Community mobilization: involvement of community
Opportunity provision: development of education and employment training
Social intervention: service linkages utilizing schools, social service agencies, families, etc.
Suppression: formal supervision and monitoring of gang activity
Organizational change and development: creation of policies and procedures

Results of the pilot in Chicago indicated that the model could be effective in reducing criminal activity and gang
involvement by the youth in the study (Spergel, 2007). The model was expanded to several other states; however,
studies revealed that for the model to be effective, it must be implemented appropriately. As we will discuss in
Chapter 10, implementation can be the most influential issue impacting treatment programs.

In 2010, the OJJDP issued a report titled Best Practices to Address Community Gang Problems. The report
advocates for the five areas identified in the Comprehensive Gang Model; however, it also notes that
communities must be organized and mobilized around the needs of youth. In that vein, the OJJDP argues for
early interventions such as prenatal and infant care and identifying at-risk elementary school children to
intervene before they may choose to join a gang. In addition, the report advocates for truancy reduction programs
and after-school activities to keep youth active and away from gang members. Finally, it advocates for
identifying gang leaders and removing them from the community while simultaneously targeting youth who were
previously gang involved and are returning home from a period of confinement (National Gang Center, 2010).
This chapter’s Featured Program box highlights PanZou, one of the programs noted in the OJJDP report.

Featured Program: PanZou: North Miami Beach Gang Reduction Program

The PanZou program targets youth, primarily of Haitian descent, in an area of the city of North Miami
Beach. Many of the residents speak only the native Haitian language of Creole. This language barrier and
a lack of services in the area represent obstacles to intervening with the youth and their families (National
Gang Center, 2010).

The program is organized around three primary efforts: prevention, intervention, and suppression.
Prevention efforts typically focus on the following:

Pairing youth with a mentor

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Teaching youth early literacy and life skills
Strengthening families by helping them to develop parenting skills
Working with schools to develop alternatives to school suspension and reduce truancy rates
Offering gender-responsive programs for girls
Increasing recreational programs in communities
Providing social skills training to youth

Intervention efforts vary but typically focus on the following:

Substance abuse counseling
Vocational training
Community service
Referral to community agencies for further treatment (e.g., substance abuse treatment)

Suppression efforts typically focus on the following:

Involving the police in foot and bike patrol of “hot spots”
Early identification of gang leaders
Gang intelligence gathering by police to proactively engage with the community
Dedicated gang unit within the police department

Another popular intervention that addressed gang involvement, the Chicago Area Project (CAP), is based on
social disorganization theory and is designed to increase community cohesion and address the issues that lead
communities to experience high levels of crime (e.g., unemployment, truancy). The CAP program includes after-
school programs, mentoring, tutoring, employment training programs, life skills, and counseling (learn more at
www.chicagoareaproject.org (http://www.chicagoareaproject.org/) ).

Gang prevention programs remain a popular option for police departments and communities across the nation.
These programs can be effective in creating community cohesion and reducing violence. To reach their
maximum potential, the programs should be multipronged and engage schools, families, community resources,
and the police.

http://www.chicagoareaproject.org/

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9.4 Juvenile Sex Offenders

The label “sex offender” is typically applied to an individual convicted of a sex offense. Sex offenses can include
sexual assault, rape, sodomy, fondling, or any forced sex act. Sex offenders are often placed on the lowest rung
of the criminal hierarchy—meaning that most people feel that sex offenders are the worst of the worst. Those
who violate children are particularly vilified. This feeling toward sex offenders also extends into prison life. Sex
offenders, particularly pedophiles, are often placed in protective custody as other inmates may choose to
victimize them in retribution for their crimes (Man & Cronan, 2001).

Note, however, that sex offenders are a heterogeneous group. There are many differences among sex offenders,
in terms of not only their victims—children, adults, men, women—but also whether they are “generalists” or
“specialists.” Generalists tend to engage in a variety of criminal behaviors (e.g., drugs, theft, sexual violence). By
contrast, specialists typically repeat the same offense type (e.g., sexual violence). It is difficult to understand
exactly who these youth are, but Finkelhor, Ormond, and Chaffin (2009) make the following observation:

The sexual behaviors that bring youth into clinical settings can include events such as sharing
pornography with younger children, fondling a child over the clothes, grabbing peers in a sexual way at
school, date rape, gang rape, or performing oral, vaginal, or anal sex on a much younger child. (p. 3)

Some researchers have argued for narrowing the definition of who should qualify as a sex offender. For example,
sexual behavior among consenting adults can include a range of deviant acts that may seem unpleasant to some
but are normal to others if shared in private. Prentky, Harris, Frizzell, and Righthand (2000) developed a risk-
assessment tool and argued that there are several different types of sex offenders: child molesters, rapists,
sexually reactive children, fondlers, paraphilics, and a combination/unknown group. Some experts argue that sex
offenders suffer from a mental disorder known as paraphilia, which is characterized by sexual arousal involving
nonhuman objects or nonconsenting persons such as children, which causes harm or distress to others (American
Psychiatric Association, 2013).

The issue of consent is complex when it comes to juvenile offenders and victims. For sex offenders, an act of
force or coercion that causes distress for others is a key condition. However, that definition is not used by states
that identify juvenile sex offenders for the purposes of classification, treatment, or supervision. In the following
sections, we will discuss laws surrounding sex offenses, typologies proposed to identify sex offenders,
characteristics of juvenile sex offenders, policies designed to prevent or address sex offending in the community,
and treatments.

Laws/Statutory Rape

A complex issue surrounding juvenile sex offending involves the ages of the offender and the victim and whether
the behavior should be labeled as a sex offense. When an adult or even an older adolescent sexually abuses a
child, it is obviously a case of sexual assault. But what if the child is 12 and the perpetrator is 14, and the 12-
year-old consented to the behavior?

The age of consent is considered the legal age at which a juvenile can consent to sexual activity. Prior to that
age, the juvenile is said not to have the mental capacity to consent to sexual activity. In most states, 16 years is
the age of consent. Statutory rape refers to sexual activity involving those who are younger than the age of
consent (Waites, 2005).

However, there are peer group exceptions (also known as close-in-age exceptions) to the age of consent rule.

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For example, what if two young people who are 14 engage in nonforced sexual activity? The law recognizes that
juveniles may experiment in sexual activity prior to the age of 16. As long as the youths are not too far apart in
age, the law provides for this peer group exception. The age difference between the victim and perpetrator is
important. In some states the difference in age is five years between the victim and the perpetrator, in others it
could be greater. An example can be found in North Carolina statutory rape law (14-27.7A):

(a) A defendant is guilty of a Class B1 felony if the defendant engages in vaginal intercourse or a
sexual act with another person who is 13, 14, or 15 years old and the defendant is at least six years
older than the person, except when the defendant is lawfully married to the person.

(b) A defendant is guilty of a Class C felony if the defendant engages in vaginal intercourse or a sexual
act with another person who is 13, 14, or 15 years old and the defendant is more than four but less than
six years older than the person, except when the defendant is lawfully married to the person.

Class B felonies are considered more serious and carry more severe sanctions. What you might notice from the
preceding statute, however, is that it only refers to youth who are 13 and older. What about those who engage in
sexual activity prior to the age of 13? Juveniles who are 12 and younger are considered unable to consent to any
sexual activity. Again, however, the courts will examine the age of both parties in deciding whether to apply the
peer group exception.

The Juvenile Sex Offender

According to David Finkelhor and colleagues (2009), juvenile offenders commit 26% of the sex offenses that
occur annually in the United States. Even so, juvenile sex offenders represent a fairly small group, at just 3.1% of
the general juvenile criminal population. Over 40% of the very young victims in both the under 6 and 7–11-year-
old categories are victimized by an offender who is under the age of 18. The vast majority of these offenders
knew their victims, as shown in Figure 9.1.

Figure 9.1: Relationship between victim and offender

In over 80% of cases, victims of sex offenses know the offenders.

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From Juveniles who commit sex offenses against minors, by D. Finkelhor, R. Ormond, & M. Chaffin, 2009.
Office of Justice Programs: Office of Juvenile Delinquency and Prevention and Prevention Child Delinquency
Bulletin Series. Washington, D.C.

Other interesting findings emerged from this survey. What types of sexual assault are juveniles committing? Of
the sexual assaults, half were acts of fondling, followed by 24% for rape, 12.5% for sodomy, 9.5% for nonforced
sex, and 4.7% for sexual assault involving an object. Finally, in terms of time of day, Figure 9.2 illustrates that
the majority of assaults occurred during the afternoon, likely when juveniles are out of school and less likely to
be supervised.

Figure 9.2: Percentage of incidents by
time of day

Only 5% of incidents involving juveniles
occur at night. In contrast, 43% occur during
afternoon hours.

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From Juveniles who commit sex offenses against minors, by
D. Finkelhor, R. Ormond, & M. Chaffin, 2009. Office of
Justice Programs: Office of Juvenile Delinquency and
Prevention and Prevention Child Delinquency Bulletin
Series. Washington, D.C.

Why do juveniles commit sex crimes? There is no easy answer to this question. The cycle of abuse is often cited
as an issue that has the potential to propel a juvenile into sexual misconduct. Studies suggest that more than 40%
of juvenile sex offenders have a history of abuse (Awad & Saunders, 1991; Phan & Kingree, 2001; Seto &
Lalumière, 2010). Some research suggests that those who suffer sexual abuse are also more likely to begin
offending at an earlier age and abuse more victims (Cooper, Murphy, & Haynes, 1996; Fox, 2017). Miner (2002)
suggests that juvenile sex offenders who start young are at greater risk for recidivism. Similarly, Langstrom and
Grann (2000) suggest that those with a prior record involving a sex offense and those with multiple victims have
an increased likelihood of recidivism.

Some studies suggest that these juveniles are more likely to have mental health disorders such as ADHD,
depression, and conduct disorder, whereas others even suggest they are more likely to be diagnosed with
psychopathy (Veneziano & Veneziano, 2002). Some studies also suggest that exposure to pornography can
increase the likelihood of deviant sexual behavior; however, recent research argues that the only increase is
among those people predisposed to be sexually aggressive (Malamuth, 2018).

Sexual Offenders: Punishment or Treatment?

Close to 80% of all rapes committed are by someone the
victim knows. Before age 18, one of three females and one of
five males will experience some degree of sexual abuse. The
judicial system must weigh whether an offender needs
custody or treatment.

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This embedded title is no longer available.

Click here (/p_Search.aspx?titleID=10319&sugg=1&rd=title) for a
list of similar titles.

1. How do you teach children to protect themselves from
abuse?

2. What would you look for if you were a counselor trying
to decide whether a sex offender is rehabilitated?

Policy Initiatives

Community notification laws are a popular way for states to hold sex offenders accountable. These laws, often
designed based on cases with adults, can impact juveniles. For example, Megan’s Law came into effect in 1996
and not only required sex offenders to register with the police but also required the police to notify the
community. Over 25 states require juveniles to register along with adults. In addition, the Adam Walsh Child
Protection and Safety Act was signed into law in 2006. This act led to SORNA, the Sex Offender Registration
and Notification Act (Office of Justice Programs, 2008).

SORNA is designed to protect the public from sex offenders because it requires a more uniform approach to
reporting requirements in each state. Prior to SORNA, most states required sex offenders to be identified on a
public register. Several provisions of SORNA are relevant for juveniles. For example, Caldwell, Ziemke, and
Vitacco (2008) note:

Juvenile offenders who offend after their 14th birthday and who were adjudicated delinquent for a
crime comparable to or more severe than aggravated sexual assault as defined by federal law (Sexual
Abuse Act of 1986) will be included in the registry. (p. 90)

In most cases, those on the registry have to reregister every three months.

The legislation also sets forth the registration length. For example, tier-three offenders—those convicted of
forcible felony sex crimes—must register for their entire lifetime (this applies to the juveniles noted previously
who are adjudicated for offenses comparable to aggravated sexual assault). In addition, as noted by Michael
Caldwell and colleagues (2008), individuals subject to SORNA registration will be required to submit to a search
of person or property at any time, with or without a warrant, on the

https://fod.infobase.com/p_Search.aspx?titleID=10319&sugg=1&rd=title

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SORNA sets certain public
registration requirements for sex
offenders. Under this act, offenders
as young as 14 may be required to
register.

C.H. Pete Copeland/Associated Press

basis of a reasonable suspicion of a violation of probation or unlawful
conduct (p. 90). This is particularly important when you consider that
juveniles are often not given jury trials and are held to different
evidentiary standards than those found in adult court. These procedural
differences could make juveniles more vulnerable to illegal search and
seizure.

The logic for this registration rests on the notion that these youth will
remain a long-term risk to the public. Yet studies suggest that the
relationship is not always clear. In fact, juvenile sex offenders have
low recidivism rates, and their sex offending history does not put them
at higher risk for future sex offenses as compared to other delinquents
(Caldwell, 2007; Zimring, Piquero, & Jennings, 2007). Another
justification for registration is the notion that juvenile sex offenders
must be significantly different from juveniles convicted of nonsex
offenses. Although some studies suggest there can be differences
(Blaske, Bordiun, Henggeler, & Mann, 1989; Fox 2017), others do not
find this to be the case (Caldwell, 2007; van Wijk et al., 2006). Think
of this in the context of the 2010 Supreme Court case Graham v.
Florida, which concluded that juveniles are less culpable due to their
brain development in the area of reasoning. If it is true that juveniles
should be held to a different standard than adults, some observers
question why the same logic should not apply to national lifetime
registries.

Perhaps most compelling is a study finding that the criteria used by
SORNA to place juveniles on sex offender registries for life failed to
predict sex offending. In other words, the youth who would be placed
on these lists were not more likely to reoffend than any other juvenile.
This research calls into question the utility of placing youth on these lists, particularly given the emotional and
social toll it takes on them and their families. Another concern is that the laws could unintentionally lead to fewer
prosecutions. For example, research suggests that prosecutors may be less likely to bring charges against a
juvenile because they are worried about the long-term ramifications for a youth who will be placed on a registry
(Letourneau, Bandyopadhyay, Sinha, & Armstrong, 2009).

In sum, these studies suggest that although juvenile sex offenders are a concern for the community, there is also a
risk that treating them punitively could have long-lasting impacts that could influence their life course.

Juvenile Sex Offenders and Rehabilitation

Cognitive behavioral therapy is considered one of the most effective treatment approaches for sex offenders.
Cognitive behavioral therapy is designed to shape behavior through a process of changing how people think
(their cognitions) and shaping their behavior through the use of rewards and punishments.

How offenders think is the focus of one particular strategy in which therapists work with clients to get them to
understand their offense cycle, which refers to the thoughts and behaviors leading to the sex offense. The
thoughts and behaviors that can lead to a sexual offense can occur over hours or maybe days, weeks, or even

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years. This process is often called grooming. Grooming, or the act of manipulating victims into complying with
a sex act, is a process that can be short or quite lengthy. Grooming behaviors often involve children, and the
offender can be an adult or another juvenile.

Often with young children, the grooming can include intimidation, games, or other incentives (Pryor, 1996). For
example, to include inappropriate touching of the victim, the perpetrator can use a game of wrestling or play. The
perpetrator can use candy, toys, or other enticements to engage with the victim. Keeping in mind that the
majority of juvenile sex offenders commit acts against someone they know (e.g., a relative or an acquaintance),
they may already have built trust with the victim prior to engaging in the sex act. But even when a juvenile is
involved in stranger rape, the decision to rape is preceded by particular thoughts and behaviors.

As we discussed earlier, sex offenders and offenses cannot be easily put into one box. In other words, not all sex
offenders go through a lengthy process in choosing their victims, particularly juveniles. However, the process or
cycle is an important one to understand because it illustrates the cognitive or psychological aspects of sex
offending.

In therapy, a counselor will often work to have the juvenile understand that criminal sexual behavior does not
just happen. In other words, the juvenile may make a statement such as, “I don’t know how it happened, before I
knew it I was doing it.” The counselor, however, will often challenge the youth’s perspective that an offense “just
happened.” Similar to any other criminal behavior, such as using drugs or alcohol, the decision is often preceded
by a series of decisions. For example, the decision to use a drug is often preceded by a decision to go to a party
where alcohol is being served or by a decision to hang out with friends who often use drugs. We refer to these
decisions and situations as high-risk thoughts or high-risk places.

Sometimes high-risk thoughts are called cognitive distortion or thinking errors. High risk in this context means
that they contribute to the probability of crime. A high-risk thought about marijuana use might be, “It is OK to
smoke marijuana because it should be legal anyway.” A high-risk situation might be attending a party where the
youth knows alcohol and drugs will be present and peer pressure to use substances will be high. These situations
and thoughts contribute to the likelihood that the youth will decide to use alcohol or drugs.

If we apply this concept to sex offending, the therapist will attempt to get juveniles to understand that their
actions and behaviors before the offense matter. For example, consider a client who meticulously planned to be
alone with the victim—either through volunteering to babysit or by taking the child to a location where they
could be alone. Did the client feel the behavior was justified as experimentation even though the victim was very
young? In other circumstances, the therapist might explore the youth’s perspective on consent and whether the
client believes that a person has the right to say no at any time, even when the victim consented at an earlier time.

Studies on the effectiveness of therapy for juvenile sex offenders are promising. In fact, studies suggest that
juvenile sex offenders are very amendable to treatment and therapy. They also underscore the idea that juveniles
are different from adults, and that we need to be mindful of tailoring services to them from a therapeutic
standpoint (Caldwell et al., 2008).

Although the results of therapy are promising, there are those who do recidivate. One of the key factors as it
relates to success in treatment is motivation. Without motivation to engage in treatment, a youth is not likely to
be successful (McGrath, 1991). Motivation may be a key issue for programs that experience high attrition rates.
In fact, a study by Hunter (2000) found that more than 50% of youth dropped out of a community-based sex-
offender treatment program in its first year of operation.

The high attrition rate noted in the Hunter study is concerning, but the vast majority of juvenile sex offenders do
not persist into adulthood. In fact, most studies find that less than 10% of juvenile sex offenders are rearrested for

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a sex-related offense.

Overall, the general public’s impression of juvenile sex offenders probably has more to do with a particularly
heinous case of sex offending highlighted on the evening news than the types of behavior that occur in reality.
There is no easy way to identify youth who are likely to recidivate, but punitive approaches are less likely to
have an impact. As can be seen throughout our discussions of all three groups of “special” offenders, treatment
and prevention hold the most promise.

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Summary of Learning Objectives

Describe the issues early starters face in their everyday lives, the characteristics of persistently disruptive
children, and the methods used to prevent and treat early delinquent behavior.

Early starters are those juveniles who begin committing crimes before the age of 14.
Early starters are more likely to have problems within their families, at school, and within their
communities.
Persistently disruptive children are those who exhibit a pattern of negative behaviors and are defiant,
disobedient, and hostile for a period of at least six months.
Identifying youth who are exhibiting disruptive behavior before they come into contact with the system
is the best way to prevent the cycle before it happens.

Explain the complexity of defining gangs and gang behavior and the relationship between gangs and crime.

Gangs are difficult to study because they are difficult to define.
Researchers suggest police consider the following criteria when deciding whether a group of youth
should be classified as a gang: symbols, communication, permanence, turf, and criminal behavior.
Girl-only gangs are uncommon; however, coed gangs are quite prevalent.
Statistics suggest that up to 48% of violent crimes in the United States may be gang related.
A number of gang-prevention and treatment programs exist, although their effectiveness is limited.

Analyze the complexity surrounding the labeling of juvenile sex offenders.

Juvenile sex offenders are a complex group of youth who can be involved in a range of sexual
behaviors.
The age of consent is a key issue with regard to juvenile sex offenders.
Sex offender notification laws are popular but tend not to be an effective way to reduce recidivism
among sex offenders.
Cognitive behavioral therapy is considered one of the most effective treatment approaches for sex
offenders.
The recidivism rates of juvenile sex offenders are low; however, early starters may have a higher
probability of recidivism.

Critical Thinking Questions
1. What is the best way to prevent the cycle of disruptive behavior in juveniles?
2. What criteria do you think should be used to decide if a group is a gang?
3. In what areas do early starters typically have problems?

Key Terms
Click on each key term to see the definition.

age of consent
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The legal age at which a juvenile can consent to sexual activity.

Comprehensive Gang Model
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An approach developed by the OJJDP to “reduce and prevent youth gang violence.”

First Steps to Success
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An intervention program that argues schools should intervene with youth as early as kindergarten.

girl gangs
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Gangs with all female members

grooming
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The act of manipulating victims into complying with a sex act.

National Youth Gang Survey (NYGS)

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The most reliable source of information on the extent of youth gangs in the United States.

offense cycle
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The process leading up to a sex offense.

PanZou
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A program that targets youth, primarily of Haitian descent, in an area of the city of North Miami Beach.

paraphilia
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A disorder characterized by sexual arousal involving nonhuman objects or nonconsenting persons such as
children, which causes harm or distress to others.

peer group exceptions
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Exceptions to the age of consent rule, also known as close-inage exceptions.

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persistently disruptive children
(http://content.thuzelearning.com/books/Johnson.5439.18.1/sections/cover/books/Johnson.5439.18.1/sections/cover/books/
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Children who exhibit a pattern of negative behaviors and are defiant, disobedient, and hostile for a period of at
least six months.

social contagion theory
(http://content.thuzelearning.com/books/Johnson.5439.18.1/sections/cover/books/Johnson.5439.18.1/sections/cover/books/
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A theory that argues that how people act in groups may be different from how they would act independently.

SORNA
(http://content.thuzelearning.com/books/Johnson.5439.18.1/sections/cover/books/Johnson.5439.18.1/sections/cover/books/
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Sex Offender Registration and Notification Act; an act designed to protect the public from sex offenders by
requiring a more uniform approach to reporting requirements in each state.

Study Group on Very Young Offenders
(http://content.thuzelearning.com/books/Johnson.5439.18.1/sections/cover/books/Johnson.5439.18.1/sections/cover/books/
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A study group formed by the OJJDP and comprised of 39 experts on the topic of child delinquency.

Youth Gang Drug Prevention Program
(http://content.thuzelearning.com/books/Johnson.5439.18.1/sections/cover/books/Johnson.5439.18.1/sections/cover/books/
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A program designed to provide funding for communities to develop coordinated efforts to reduce gang problems
in their areas.

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