E M P I R I C A L R E S E A R C HYouth Pathways to Placeme.docx

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: [email protected]
M. A. Lopez
J. R. Sorensen
Department of Criminal Justice, East Carolina University,
Rivers Building, Office #245, Mail Stop 505, Greenville,
NC 27858, USA

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 disorder.

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
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
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***
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
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

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
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
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.
References
Abram, K., Teplin, L., McClelland, G., & Dulcan, M. (2003).
Comorbid psychiatric disorders in youth in juvenile detention.
Archives of General Psychiatry, 60(11), 1097–1108.
American Bar Association & National Bar Association. (2001).
Justice by gender: The lack of appropriate prevention, diversion

and treatment alternatives for girls in the justice system.
Washington, DC: Author.
Archer, R., Stredney, R., Mason, J., & Arnau, R. (2004). An
examination and replication of the psychometric properties of
the Massachusetts Youth Screening Instrument: Second Edition
(MAYSI-2) among adolescents in detention settings.
Assessment,
11(4), 290–302. doi:10.1177/1073191104269863.
Belknap, J., & Holsinger, K. (2006). The gendered nature of
risk
factors for delinquency. Feminist Criminology, 1(48), 48–71.
doi:10.1177/1557085105282897.
Bergman, M., & Scott, J. (2001). Young adolescent’s wellbeing
and
health-risk behaviors: Gender and socio-economic differences.
Journal of Adolescence, 24(2), 183–197. doi:10.1006/jado.
2001.0378.
Boots, D. (2008). Mental health and violent youth: A
developmental/
lifecourse perspective. New York: LFB Scholarly Publishing.

Boots, D., & Wareham, J. (2009). An exploration of DSM-
oriented
scales in the prediction of criminal offending among urban
American youths. Criminal Justice and Behavior, 36(8),
840–860. doi:10.1177/0093854809337714.
Bottcher, J. (2001). Social practices of gender: How gender
relates to
delinquency in the everyday lives of high-risk youths. Crimi-
nology, 39(4), 893–931.
Brame, R., Paternoster, R., Mazerolle, P., & Piquero, A. (1998).
Testing for the equality of maximum-likelihood regression
coefficients between two independent equations. Journal of
Quantitative Criminology, 14, 245–261.
Chesney-Lind, M. (2002). Criminalizing victimization: The
unin-
tended consequences of pro-arrest policies for girls and women.
Criminology & Public Policy, 2(1), 81–90. doi:10.1111/j.1745-
9133.2002.tb00108.x.
Compton, S., Burns, B., Egger, H., & Robertson, E. (2002).
Review of

the evidence base for treatment of childhood psychopathology:
Internalizing disorders. Journal of Consulting and Clinical
Psychology, 7(6), 1240–1266.
Copeland, W., Miller-Johnson, S., Keeler, G., Angold, A., &
Costello, J.
(2007). Childhood psychiatric disorders and young adult crime:
A
prospective, population-based study. American Journal of
Psychi-
atry, 164(11), 1668–1675. doi:10.1176/appi.ajp/2007.06122026.
Crimmins, S., Cleary, S., Brownsteing, H., Spunt, B., & Warley,
B.
(2000). Trauma drugs and violence among juvenile offenders.
Journal of Psychoactive Drugs, 32(1), 43–54.
doi:10.1080/02791
072.2000.10400211.
Davis, M., Fisher, W., Gershenson, B., Grudzinskas, A., &
Banks, S.
(2009). Justice system involvement into young adulthood:
Comparison of adolescent girls in the public mental health
system and in the general population. American Journal of

Public Health, 99(2), 234–236. doi:10.2105/AJPH.2008.141135.
Felitti, V. J., Anda, R. F., Nordenberg, D., Williamson, D. F.,
Spitz, A.
M., Edwards, V., et al. (1998). Relationship of childhood abuse
and household dysfunction to many of the leading causes of
death
in adults: The Adverse Childhood Experiences (ACE) Study.
American Journal of Preventive Medicine, 14(4), 245–258.
Ford, J., Chapman, J., Mack, M., & Pearson, G. (2006).
Pathways
from traumatic child victimization to delinquency: Implications
for juvenile and permanency court proceedings and decisions.
Juvenile and Family Court Journal, 57(1), 13–26. doi:
10.1111/j.1755-6988.2006.tb00111.x.
Gavazzi, S., Yarcheck, C., & Chesney-Lind, M. (2006). Global
risk
indicators and the role of gender in a juvenile detention sample.
Criminal Justice and Behavior, 33(5), 597–612. doi:10.1177/
0093854806288184.
Gilligan, C., & Brown, L. (1992). Meeting at the crossroads.
New

York, NY: Ballantine.
123
http://dx.doi.org/10.1177/1073191104269863
http://dx.doi.org/10.1177/1557085105282897
http://dx.doi.org/10.1006/jado.2001.0378
http://dx.doi.org/10.1006/jado.2001.0378
http://dx.doi.org/10.1177/0093854809337714
http://dx.doi.org/10.1111/j.1745-9133.2002.tb00108.x
http://dx.doi.org/10.1176/appi.ajp/2007.06122026
http://dx.doi.org/10.1080/02791072.2000.10400211
http://dx.doi.org/10.1080/02791072.2000.10400211
http://dx.doi.org/10.2105/AJPH.2008.141135
http://dx.doi.org/10.1177/0093854806288184
http://dx.doi.org/10.1177/0093854806288184
Goodkind, S., Ng, I., & Sarri, R. (2006). The impact of sexual
abuse
in the lives of young women involved or at risk of involvement
in the juvenile justice system. Violence Against Women, 12(5),
456–477. doi:10.1177/1077801206288142.
Griffin, P. (2002). The Post-traumatic stress disorder project.
Penn-
sylvania Progress: Juvenile Justice Achievement in Pennsylva-

nia, 9(2), 1-10. Washington, DC: National Center for Juvenile
Justice.
Grisso, T. (2004). Double jeopardy: Adolescent offenders with
mental
disorders. Chicago, IL: University of Chicago Press.
Grisso, T., & Barnum, R. (2006). Massachusetts Youth
Screening
Instrument- Version 2: User’s Manual and Technical Report.
Sarasota, FL: Professional Resource Press.
Hennessey, M., Ford, J., Mahoney, K., Ko, S., & Siegfried, C.
(2004).
Trauma among girls in the juvenile justice system. Los Angeles,
CA: National Child Traumatic Stress Network.
Hogg Foundation for Mental Health. (2010). Trauma-informed
care.
Retrieved from
www.hogg.utexas.edu/uploads/documents/Trauma_
Inform_Care_091410.pdf.
Huckshorn, K. (2006). Re-designing state mental health policy
to
prevent the use of seclusion and restraint. Administration and

Policy in Mental Health, 33(4), 482–491. doi:10.1007/s10488-
005-0011-5.
Kazdin, A. (2005). Parent Management Training: Treatment for
oppositional, aggressive, and antisocial behavior in children and
adolescents. New York, NY: Oxford University Press.
Lynam, D., Caspi, A., Offitt, T., Wikström, P.-O., Loeber, R., &
Novak, S. (2000). The interaction between impulsivity and
neighborhood context on offending: The effects of impulsivity
are stronger in poorer neighborhoods. Journal of Abnormal
Psychology, 109(4), 563–574.
MacDonald, J., & Chesney-Lind, M. (2001). Gender bias and
juvenile
justice revisited: A multiyear analysis. Crime and Delinquency,
47(2), 173–186. doi:10.1177/0011128701047002002.
McMackin, R., Morrissey, C., Newman, E., Erwin, B., & Daley,
M.
(1998). Perpetrator and victim: Understanding and managing the
traumatized young offender. Corrections Management Quar-
terly, 2(1), 36–45.

Monahan, J., Steadman, H., Robbins, P., Appelbaum, P., Banks,
S.,
Grisso, T., et al. (2005). An actuarial model of violence risk
assessment for persons with mental disorders. Psychiatric
Services, 56(7), 810–815.
National Child Traumatic Stress Network. (2012). Creating
trauma
informed systems. Retrieved from http://www.nctsn.org/
resources/topics/creating-trauma-informed-systems.
Pavkov, T., George, R., & Czapkowicz, J. (1997). Predictors of
length
of stay among youths hospitalized in state hospitals in Illinois.
Journal of Child and Family Studies, 6(2), 221–231. doi:
10.1023/A:1025054825467.
Prescott, L. (1997). Adolescent girls with co-occurring
disorders in
the juvenile justice system. Delmar, NY: The GAINS Center.
Rosenfield, S., Lennon, M., & White, H. (2005). Mental health
and
the self: Self-salience and the emergence of internalizing and

externalizing problems. Journal of Health and Social Behavior,
46(4), 323–340.
Sampson, R. J., & Laub, J. H. (2005). A life-course view of the
development of crime. The Annals of the American Academy of
Political and Social Science, 602, 12. doi:10.1177/0002716
205280075.
Schwank, J., Espinosa, E., & Tolbert, V. (2003). Mental health
and
juvenile justice in Texas. Austin, TX: Texas Juvenile Probation
Commission.
Sedlak, A., & McPherson, K. (2010). Survey of youth in
residential
placement: Youth’s needs and services (SYRP Report). Rock-
ville, MD: Westat.
Sherman, F. (2005). Detention reform and girls: Challenges and
solutions. Baltimore, MD: The Annie E. Casey Foundation.
Retrieved from www.aecf.org/publications/data/jdai_pathways_
girls.pdf.
Sickmund, M., Sladky, T.J., & Kang, W. (2004). Census of
juveniles

in residential placement databook. Retrieved from www.ojjdp.
ncjrs.org/ojstatbb/cjrp/.
Skeem, J., Emke-Frances, P., & Louden, J. (2006). Probation,
mental
health, and mandated treatment: A national survey. Criminal
Justice and Behavior, 33(2), 158–184. doi:10.1177/009385
4805284420.
Snyder, H. (2008). Juvenile Arrests 2006. Bulletin. Washington,
DC:
U.S. Department of Justice, Office of Justice Programs, Office
of
Juvenile Justice and Delinquency Prevention.
Snyder, H., & Sickmund, M. (2006). Juvenile offenders and
victims:
2006 National report. Washington, DC: Office of Juvenile
Justice and Delinquency Prevention.
Solomon, P., Draine, J., & Marcus, S. (2002). Predicting
incarceration
of clients of a psychiatric probation & parole service.
Psychiatric
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.
Texas Juvenile Justice Department (2012) Strategic plan: 2013–
2017.
Retrieved from http://www.tjjd.texas.gov/publications/reports/
TJJD%20Strategic%20Plan%20-%20FINAL%20-%20JULY%
202012.pdf.
Texas Juvenile Probation Commission. (2010). The state of
juvenile
probation activity in Texas: Statistical and other data on the
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.pdf.

Thornberry, T., Huizinga, D., & Loeber, R. (2004). The causes
and
correlates studies: Findings and policy implications. Juvenile
Justice, 9(1), 3–19.
Wasserman, G., McReynolds, L., Ko, S., Katz, L., & Carpenter,
J.
(2005). Gender differences in psychiatric disorders at Juvenile
Probation Intake. American Journal of Public Health, 95(1),
131–137.
Zoccolillo, M., Tremblay, R., & Vitaro, F. (1996). DSM-IIIR &
DSM-
III criteria for conduct disorder in preadolescent girls. Journal
of
the American Academy of Child and Adolescent Psychiatry,
35(4),
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
123
http://dx.doi.org/10.1177/1077801206288142
http://www.hogg.utexas.edu/uploads/documents/Trauma_Inform
_Care_091410.pdf
http://www.hogg.utexas.edu/uploads/documents/Trauma_Inform

_Care_091410.pdf
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.pdf
http://www.aecf.org/publications/data/jdai_pathways_girls.pdf
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%20Strategi
c%20Plan%20-%20FINAL%20-%20JULY%202012.pdf
http://www.tjpc.state.tx.us/publications/reports/RPTSTAT2008.
pdf
http://www.tjpc.state.tx.us/publications/reports/RPTSTAT2008.
pdf
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.
123
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Title
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2. What are some key personality features that define you?
3. Are your personality features consistent, or do they change
according to the situation?
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personality? What do all psychoanalytic theories have in
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Complete the following table:
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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: [email protected]
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
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.

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

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