Services for Juveniles With Emotional Disturbances in Secure-Care Settings: An Exploratory Analysis of Racial Disparities and Recidivism

Abstract

This study examines associations between recidivism rates and groups/programs for legally mandated education, behavioral, and mental health services that court-ordered juvenile youth (“juveniles”) with identified emotional disturbance or related conditions receive in secure-care juvenile facilities. Using statewide agency data in Minnesota, this exploratory analysis investigates whether there are racial/ethnic and county disparities in residential placements or secure-care settings via programs and groups for court-ordered juveniles (N = 1,092). The study also considers whether program and group placements contribute to racial/ethnic patterns of recidivism. This study finds that recidivism rate differences by program/group level are largely attributed to differences in racial/ethnic compositions. Findings suggest placements lack strong effects on recidivism, and assignments may perpetuate inequalities. In addition, from this exploratory analysis examination, this article offers considerations for future research-to-practice partnerships to strengthen legally and policy-mandated program and service delivery with practices that increase training to juvenile justice system professionals, access to secure-care setting services, and transition services for juveniles.

Keywords

juvenile justice disability service delivery policy recidivism

Students with disabilities who are detained or incarcerated within the juvenile justice system as juvenile youth (“juveniles”) are consistently overrepresented relative to their counterparts without disabilities (Thompson & Morris, 2016). Moreover, for detained or incarcerated youth with emotional disturbance (ED), which can comprise such related behavioral conditions as aggression, self-injurious behavior, or anxiety disorders (Center for Parent Information & Resources, 2019), there are particular disadvantages as juvenile justice systems across the United States struggle to provide access to high-quality special education services (Gagnon et al., 2015; Leone et al., 2002; Leone & Weinberg, 2012). Inadequate mental health services and behavioral treatment for juveniles are additional concerns (Quinn et al., 2005; Swank & Gagnon, 2016, 2017). Juveniles within the juvenile justice system are also about 10 times more likely than their counterparts outside the system to have ED or related conditions (Fazel et al., 2008). Also, approximately 65% to 70% of juveniles within the juvenile justice system have a diagnosable mental health condition, emotional disturbance, or behavioral disorder (National Center for Mental Health and Juvenile Justice, 2013). Most strikingly, in the U.S. juvenile justice system that detains approximately 48,000 minor youth on any given day (Prison Policy Initiative, 2019), there is a missing link as to which types of correctional services, programs, and individual-level risk factors are most strongly associated with reduced recidivism for juvenile justice system-involved youth (Zhang et al., 2011).

Given the overrepresentation of racial or ethnic minorities within the juvenile justice system, there is also a greater need to understand more about the types of programs and services they receive, particularly as they are overrepresented within the juvenile justice system population. Specifically, Black youth are approximately 14% of the overall U.S. youth population but approximately 77% of the juvenile justice system population (Prison Policy Initiative, 2019). Similarly, Indigenous youth make up less than 1% of the overall U.S. youth population but approximately 5% of youth within the juvenile justice system (Prison Policy Initiative, 2019). Yet, for racial and ethnic minority juveniles, there are further concerns about the association, if any, with recidivism after community reentry. Juvenile recidivism—which can occur as a conviction for a new offense, an arrest for a new offense, or as a probation or supervision revocation (Maxfield, 2005)—remains an area of emerging research (Barrett & Katsiyannis, 2015). However, studies do suggest that racial or ethnic minority juveniles have less access to correctional services or programs most strongly associated with reduced recidivism (Zhang et al., 2011). This is particularly true for racial or ethnic minority juveniles with an ED classification (Barrett & Katsiyannis, 2015).

Moreover, the lack of access to services for juveniles with disabilities, racial disparities, and recidivism challenges for youth remain even after four decades of juvenile justice and education policy reforms within federal law (Barrett et al., 2010; Sinclair et al., 2017). These relevant laws include the Juvenile Justice and Delinquency Prevention Act [JJDPA], the Individuals with Disabilities Education Improvement Act [IDEA], and the Every Student Succeeds Act [ESSA]) that address outcome disparities for one of the most vulnerable populations within the juvenile justice system. For example, all IDEA-eligible juveniles must receive a free, appropriate public education (FAPE), outlined via an Individualized Education Program (IEP) and/or an Individualized Transition Program (ITP). These mechanisms outline services, accommodations, supports (e.g., employment or transition supports post-release), and individualized measurable academic goals (IDEA, 20 U.S.C. § 1400 et seq.). In addition, each eligible juvenile must receive the full extent of his or her due process protections under IDEA (IDEA, 2004). The JJDPA further requires that states invest federal funds to address juvenile justice and future delinquency prevention, meet education needs of juveniles, and provide for gender-specific programs and mental health services (JJDPA, 1974). Finally, the recent reauthorization of ESSA now includes a larger focus on the transition of students from public schools to juvenile justice placements and back to appropriate educational settings upon reentry to the community (ESSA, 2015). It requires prompt credit transfer and mandates assistance with high school diploma acquisition (ESSA, 2015). As evident here, these pieces of legislation are important not only because they outline federal guidance and mandates to states, State Education Agencies (SEAs), and Local Educational Agencies (LEAs) but also provide critical insight regarding what constitutes the wide range of services offered to juveniles to address transition, community re-entry, and recidivism (Strassfeld, 2021).

Factors Associated With Recidivism for Juvenile Youth With Emotional Disturbances

Although law and policy serve as important mechanisms to guide service delivery within the juvenile justice system and aid in the reduction and prevention of recidivism, they are not the only factors that have associations with juvenile recidivism. While juvenile recidivism rates and associated reporting requirements vary widely from state to state, the limited research in this area finds that juveniles with identified ED or related conditions are most likely to recidivate (Barrett & Katsiyannis, 2015). Moreover, the emerging research suggests that there is an array of recidivism risk factors, particularly for youth with ED including individual, system, and placement-related factors.

Individual-level risk factors

At the individual level, studies have found that a strong predictor for recidivism is age at first arrest for any juvenile (Barrett et al., 2010; Becker et al., 2012). In a multi-cohort replication study of predictors of recidivism and offense severity, Barrett et al. (2010) found that those arrested at a younger age are more likely to recidivate. Furthermore, other studies have found that girls with a history of ED or related conditions during elementary school were more likely to be arrested by middle or high school (Gage et al., 2012). Relatedly, within the limited research on neighborhood and community effects on juvenile recidivism, studies have found that proxy variables such as neighborhood unemployment and poverty levels can have an association with recidivism (Grunwald et al., 2010). However, studies have noted that neighborhood and/or county variables often serve as suboptimal proxies for more specific, individual-level variables (e.g., household income, family history of recidivism) due to data collection restrictions within the juvenile justice system (Ng, 2010).

In addition, the intensity and type of education, behavioral, or mental health services that a juvenile receives during their stay or detainment is also associated with risk for recidivism. For instance, studies suggest that juveniles enrolled in high-quality education programs and those with higher rates of academic attainment during detainment may be more likely to return to school after reentry into their community and less likely to recidivate (Blomberg et al., 2011; Katsiyannis et al., 2008). Similarly, comorbid mental and behavioral health needs have been associated with higher rates of recidivism (Hoeve et al., 2013). Finally, as previously mentioned, the emerging body of literature in this area finds that an ED identification is a stronger predictor of recidivism for Black juveniles than it is for White juveniles (Barrett & Katsiyannis, 2015).

System-level risk factors

At the system level, evidence suggests that the resources and treatment quality within a particular juvenile justice system can serve as a risk factor for recidivism (Baglivio et al., 2018). For instance, when juvenile justice systems under-identify children with psychiatric disorders who might be otherwise eligible for services under IDEA or a mental or behavioral health program specifically targeting juveniles with ED or related conditions, those youth may be at even greater risk for recidivism (Quinn et al., 2005). Similarly, other studies have found that when juveniles transition out of the juvenile justice system and are not provided individualized and robust services (e.g., services that meaningfully prepare youth for transition, community reentry, additional education attainment, or social skills development), they are more likely to recidivate (Bullis et al., 2002).

Placement risk factors

Finally, within the limited research examining effects of placement for juveniles, program and/or group placement for juveniles within the juvenile justice system can also have negative associations with recidivism. That is, once a youth is within the juvenile justice system, the types of placements (e.g., residential, secure-care settings, group homes, or boot camps) and groups (e.g., groupings by gender or grade level) may also have associations with a greater likelihood of recidivism (Baglivio et al., 2018). For example, in a study examining juvenile justice involvement, youth randomly assigned to Multidimensional Treatment Foster Care had lower levels of juvenile youth association than those youth assigned to group home placements at a 12-month follow-up (Leve & Chamberlain, 2005). Similarly, studies have found that juveniles with behavioral issues were more likely to be in restricted, incarcerated settings, while youth with emotional issues were more likely to be placed into residential treatment placements (Lyons et al., 2001).

Regarding race and ethnicity, other studies have found that professionals’ bias or racism can influence program referral and placement decisions wherein Black youth are more likely to be referred to more restrictive programs than White youth (Campbell et al., 2018). Racial bias within referral and placement decisions is particularly relevant within a juvenile justice system context as professionals can make specific placement determinations after a judge’s adjudication. Intake, in particular, is where previous research has found the “greatest evidence of racial bias” (Leiber & Mack, 2003, p. 57). These referral and placement decisions are also associated with recidivism increases for Black youth (Campbell et al., 2018).

Even though several risk factors associated with recidivism for juveniles with behavioral conditions have been found within the limited research base, the dearth of research itself contributes to the challenges of both understanding issues within the field and then subsequently addressing them (Lane et al., 2012). These challenges are often due to barriers such as juvenile justice facility center attrition and lack of facility coordination or research-practice partnerships (RPP) that are “long-term collaborations between practitioners and researchers that are organized to investigate problems of practice and solutions for improving schools and school districts” (Coburn & Penuel, 2016, p. 48). Therefore, juvenile justice research, particularly for youth with mental health issues in secure-care residential settings, often offers a view that identifies barriers and challenges to service delivery but does not always offer robust recommendations to “improve on standard practice” (Kumm et al., 2019, p. 250).

Thus, as evident here, the juvenile justice system is guided by a set of relevant laws and policies supporting service delivery, mental and behavioral treatment, and supports for juveniles. However, it is apparent that juveniles experience a variety of recidivism risk factors within the juvenile justice system due to their age, facility resources, or potential racism/bias within the juvenile justice system itself, and placement. In addition, the limited research base necessitates the “[i]mplementation of empirical studies in these facilities [to] help grow the body of research in this area” (Lane et al., 2012, p. 50).

Yet, the small research base provides limited information regarding the degree of association between placement in a particular type of setting and recidivism. Due to the prevalence of court-ordered secure-care setting placements (e.g., behavioral and mental health treatment and interventions and cognitive behavior treatment) for juveniles with ED or related conditions (Gagnon & Barber, 2010; Stone & Zibulsky, 2015), the present study explored associations between the types of programs and services for behavioral and mental health services that court-ordered juveniles with ED or related conditions receive in secure-care settings. It also examined the likelihood of recidivism upon release, with particular attention paid to race and ethnicity due to a lack of research examining trends for system-involved racial or ethnic minority youth with ED or related conditions.

Research Questions

Within this exploratory analysis, the research questions are as follows: (a) Among juveniles, what are rates of recidivism by group, program, and race/ethnicity? (b) Among juveniles, are there racial/ethnic differences in residential, secure-care (“secure-care”) placement settings via programs and groups? and (c) Do program and group differences in rates of recidivism remain after considering their racial/ethnic composition and youths’ county of residence? To answer these questions, data from the State of Minnesota, a racially diverse state, are examined.

Method

Setting and Participant Characteristics

In Minnesota, both juvenile placement in secure-care facilities and reentry services are overseen by the Minnesota Department of Corrections (Swayze & Buskovick, 2012). Mental health screenings may be required for any juvenile who is detained in a state-licensed detention facility (Swayze & Buskovick, 2012). Prior to or in lieu of sentencing to a juvenile or adult correctional facility, juveniles who have committed an offense or crime can be referred to a diversion program. Diversion is a process whereby less serious offenders (e.g., juveniles who have committed petty, non-violent crimes) are diverted to programs and services offered outside of “traditional” corrections institutions that serve adult or juvenile populations (Swayze & Buskovick, 2012). The goal of diversion is to reduce juvenile delinquency and recidivism rates while maintaining public safety (Swayze & Buskovick, 2012). Referrals to diversion in Minnesota can come from a variety of sources, including county attorneys, school administrators, and probation officers (Swayze & Buskovick, 2012). Each juvenile in this study had been diverted to a secure-care residential facility setting because of an existing, identified ED or related condition. Within the sample, each juvenile was followed until the 5 years following their release from secure-care detainment or at the point of the first adult criminal conviction.

The sample (N = 1,092) consisted of all enrolled juveniles with identified ED or related conditions born between 1984 and 2001 who were court-ordered to receive services from the agency during their period of detainment. Overall, the Minnesota sample included the following demographic characteristics: 76.94% male, an average age of 16.14 years (SD = 1.48 years), 39.62% White, 35.47% Black, 1.26% Latinx, 11.64% Indigenous, 3.97% Asian/Asian-Pacific Islander, and 6.95% with Two or More Races (see Note 1).

Dataset and Dataset Characteristics

The dataset for this study came from a statewide agency (“agency”) in Minnesota that provides court-ordered services to juveniles in juvenile justice residential, secure-care facilities throughout the state. The agency is a trauma-informed organization that provides, among other services, special education, youth mental health services and treatment, trauma-focused residential treatment, social development, and relationship-building programs for all court-ordered juveniles. Under the federal definition, these facilities are considered secure-care because juveniles were court-ordered to receive residential services at these facilities that have “building features and/or staffing models designed to restrict the movement and activities of the residents” (U.S. Departments of Education & Justice, 2014, p. 2).

The agency data were collected by the agency (and not the authors of this article), so the age range of juveniles and data collection dates for juveniles within the study were pre-determined by the particular needs of the agency. Data were collected yearly from the time of each juvenile detainment, so no data from a youth’s prior case file or juvenile record were provided. The authors received approval from the Institutional Review Board prior to any data analysis of this previously collected data.

Each juvenile who received services from this agency had both a group affiliation and program assignment for services. Juveniles were assigned to a group based on gender and age, but subjective factors such as current size of existing groups, offense-level statuses within existing groups, and maturity levels of overall existing group members were also used to determine group placement for incoming youth. It is important to note that group affiliation determinations were predominately subjective. For example, if two similar-age juveniles (one of whom provided information to law enforcement about the other juvenile’s involvement in a separate incident) came to the facility at the same time, these youth would be placed in separate groups.

Juveniles were placed into one of the four programs based on his or her previously identified behavioral, education, mental health, or sentencing needs found during an intake assessment. Program Cognitive Management is a behavioral and emotional management program that uses cognitive behavioral approaches to target juveniles with behavioral needs to reduce out-of-home placement. Program Long-Term Treatment is a residential treatment program that uses evidence-based practices to address behavioral, emotional, chemical dependency, and mental health needs for juveniles who will be detained over longer periods of time. Program Independent Living is a gender-specific transition program that targets independent-living skills and provides education, employment, and life skills services. A fourth program, Program Short-Term Treatment, which is a short-term intervention program (between 90–120 days) with a focus on behavior management, self-regulation, and outpatient substance abuse and dependency treatment, was also an option. However, given the small number of participants in the program, it was excluded from analyses (see Note 2).

Exploratory Data Analysis

Derived from the work of John Tukey, Exploratory Data Analysis (EDA) is a method used to examine trends and patterns, if any, within actualized, real-time data (Behrens & Yu, 2003). EDA is particularly useful when the “goal is not to draw conclusions regarding guilt and innocence but rather to investigate the actors, generate hunches, and provide preliminary evidence” (Behrens, 1997, p. 133). Specifically, EDA is used as a methodological tool to examine “numerous hypotheses, [look] for patterns, and [suggest] hypotheses based on the [data]” (Behrens, 1997, p. 133). Moreover, a goal of EDA is to develop models that help explain or clarify a phenomenon, and this can be accomplished using EDA as a data-analysis tool that complements other data methodology (Behrens & Yu, 2003). Accordingly, the preliminary evidence examined here is from one component of the juvenile justice system (i.e., secure-care settings) and is offered as a way to explore patterns, particularly regarding racial disparities, found within this evidence.

Furthermore, EDA is useful as a reporting tool for exploratory findings in areas where there is limited prior comprehensive reporting and/or longitudinal findings (Behrens & Yu, 2003). Therefore, within this article, it is helpful to examine these specific data in terms of the posed research questions and to understand how placement and program-based decisions may influence recidivism over time. In addition, EDA provides an opportunity to explore the extent of disparity, if any, and make deductions as to any potential causal relationships between factors that should be noted for future research.

Variables

The dependent variable recidivation is a binary variable based on whether the juvenile who formerly received services in one of the three diversion programs was charged and convicted of a new offense after initial release. The independent variables included program-, group-, and individual-level characteristics. A categorical variable represents the program that the juvenile is enrolled in: Program Cognitive Management (the reference group), Program Long-Term Treatment, and Program Independent Living. An indicator variable represents the group that the juvenile is then assigned to: Group Atlas (the reference group), and Groups Breckenridge, Cadre, Delta, Emblazon, Feature, and Gable. A six-category variable represents the race/ethnicity of the youth: White (the reference group), Black, Latino (referred to here as Latinx), Indigenous, Asian/Asian-Pacific Islander, and Two or More Races. A binary variable represents whether the juvenile is listed as female (coded “1” if female and “0” if male) and a continuous variable represents the age of the juvenile. Finally, a categorical variable represents the county (out of 64 counties) in which the juvenile originally resided. More information about the sample can be found in Table 1, which shows descriptive statistics for all variables used in the analyses. Table 1 shows the proportion of individuals who recidivate (10% of our sample), attend different programs and groups, belong to different racial/ethnic groups, and are female. It also shows the average age of individuals.

Table 1.

Descriptive Statistics on All Variables Used in Analyses.

Variables	M/Proportion	(SD)	n
Dependent variable
Recidivate	0.10		185
Independent variables
Program
Cognitive Management	0.50		603
Long-Term Treatment	0.40		470
Independent Living	0.10		125
Group
Atlas	0.22		265
Breckenridge	0.15		182
Constellation	0.29		351
Delta	0.08		105
Emblazon	0.08		104
Feature	0.08		115
Gable	0.06		71
Youth race/ethnicity
White	0.40		439
Black	0.35		393
Latinx	0.01		14
Indigenous	0.12		129
Asian/Asian-Pacific Islander	0.04		44
Two or More Races	0.07		77
Female	0.24		293
Age	16.14	(1.49)

Note. 20 students were also enrolled in Short-Term Treatment, but were dropped due to small N.

Analytic Strategy

Using the Minnesota data, this study further investigated whether there are descriptive differences in rates of recidivism by program, group, and juvenile race/ethnicity. The analyses first showed a series of descriptive figures that displayed rates of recidivism by student characteristics and the racial/ethnic composition of programs and groups. To examine the link between recidivism and the racial/ethnic composition of programs and groups, regression analyses were conducted. In these analyses, given that counties differ drastically by socioeconomic and demographic compositions—characteristics that are linked with rates of recidivism—county fixed effects models were used to estimate rates of recidivism. Therefore, only outcomes for juveniles that reside in the same county were compared. The model to be fitted is shown in the following:

l n \frac{P r (y_{i j} = 1)}{1 - P r (y_{i j} = 1)} = α + χ_{i j}^{'} β + ν_{i} + ε_{i j}

where i = 1, . . ., N denotes individuals, j = 1, . . ., n_i denotes counties of residence, and

$l n \frac{P r (y_{i j} = 1)}{1 - P r (y_{i j} = 1)}$ is whether the juvenile recidivates.

α is the intercept.

β is the vector of regression coefficients.

χ_ij is the vector of individual-level predictors, including race, program and group indicators, and control variables (e.g., sex and age).

ν_i is the individual-level residual.

ε_ij is the county-level residual.

The fixed effects approach estimates α and β using logistic regression where ${\bar{y}}_{i} = \sum_{j = 1}^{n_{i}} y_{i j} / n_{i} a n d \bar{y} = \sum_{i = 1}^{N} y_{i j} / N$ are the within-individual and overall means and similarly for the other terms. Standard errors are estimated with a Huber–White sandwich estimator.

Results

Turning to the first research question, which focuses on descriptive differences on the outcome variable (i.e., youth recidivism) by key variables of interest, Figure 1 shows rates of recidivism by program, group, and race/ethnicity within the Minnesota dataset. Here, the main finding is that different programs, groups, and racial/ethnic groups have, descriptively, different rates of recidivism. Among programs, Cognitive Management has the lowest rates of recidivism (12%) compared to Long-Term Treatment (18%) and Independent Living (21%). For groups, Atlas and Breckenridge have far lower rates of recidivism (4% and 1%, respectively) compared to other groups, such as Emblazon (33%) or Gable (30%). Black and Indigenous youth also have higher rates of recidivism (22% and 19%, respectively) compared to other race/ethnicity categories.

Figure 1.

Rates of recidivism, by group, program, and race/ethnicity.

The second research question asks whether there are racial/ethnic differences in program and group placements. To address this question, Figure 2 shows the racial/ethnic composition of programs and Figure 3 shows the racial/ethnic composition of groups. These figures demonstrate that the programs and groups have largely different racial/ethnic compositions. For example, almost half of the juveniles who entered the Cognitive Management program were White, compared to less than a third of juveniles who were sentenced to Program Independent Living. Program Cognitive Management contained the fewest number of Black juveniles (27%) compared to Program Independent Living (45%). Among groups, the percentage of White juveniles ranged from 21% (Group Emblazon) to 51% (Group Breckenridge). These groups also had the highest and lowest percentage of Black juveniles, respectively. Descriptive results show that rates of recidivism varied widely by programs, groups, and racial/ethnic categories. Moreover, programs and groups also had different racial/ethnic compositions, which suggests that program and group differences in rates of recidivism may be linked to their demographic composition. Alternatively, White juveniles may be assigned to more effective programs.

Figure 2.

Racial/ethnic demographics of programs.

Figure 3.

Racial/ethnic demographics of groups.

The final research question focuses on whether program and group differences in recidivism rates persist after considering their demographic compositions. Accordingly, Table 2 shows coefficients from logistic regression models with county fixed effects. Model 1 includes only a variable representing programs to test whether program differences in rates of recidivism persist after only comparing youth that come from the same county. Model 2 adds variables representing groups, which reveals whether program differences persist once group placements are considered, as well as group differences themselves. Model 3, the full model, includes student characteristics, notably race/ethnicity, to examine whether program and group differences remain after considering their demographic compositions.

Table 2.

Coefficients From County Fixed Effects Logistic Regression Models Estimating Whether Juvenile Recidivates.

Variables	Model 1	Model 2	Model 3
Program (reference group: Cognitive Management)
Long-Term Treatment	2.80*	−0.98*	−1.00
Independent Living	3.02*	−9.66	−8.87
Group (reference group: Atlas)
Breckenridge		−0.75	−0.40
Constellation		1.74***	2.14
Delta		2.68***	3.21
Emblazon		2.90***	3.04
Feature		2.44***	2.93
Gable		11.53	10.75
Race/ethnicity (reference group: White)
Black			0.76**
Latinx			−0.36
Indigenous			1.44***
Asian/Asian-Pacific Islander			−0.85
Two or more race			−0.37
Female			0.34
Age			0.19*
Observations	1,155	1,155	1,155

p < .05. **p < .01. ***p < .001.

From this analysis, there are two main findings. First, while program and group differences are similar in Models 1 and 2, once juvenile characteristics of race, gender, and age are controlled for, there are no differences in rates of recidivism among programs and groups (Model 3). Second, Model 3 also shows that Black and Indigenous juveniles were much more likely to recidivate regardless of the type of group or program services they received. Interaction models were run to determine whether there were differential returns to programs and groups by race/ethnicity. No interaction terms were significant. Figure 4 illustrates this finding: The highest probabilities of recidivating were found for Black (0.23) and Indigenous (0.32) juveniles.

Figure 4.

Predicted probabilities of recidivating.

Discussion

The present study attempted to identify factors associated with recidivism for juveniles with identified ED or related conditions by analyzing a longitudinal dataset covering multiple cohorts of juveniles court-ordered to receive education, mental health, and behavioral services within residential, secure-care settings. Three research questions were posed. First, in regard to rates of recidivism by group, program, and youth race/ethnicity, the study found that, descriptively, rates differed across these three categories. This is consistent with prior work (Becker et al., 2012). For instance, Black and Indigenous juveniles were found to have higher rates of recidivism than other racial/ethnic groups. Second, regarding whether the racial/ethnic composition of programs and groups differed, some groups had more than double the proportion of White youth compared to others. Third, regarding whether program and group differences in rates of recidivism persisted after considering their racial/ethnic composition, the study found that there are no longer statistically significant program and group differences in rates of recidivism after controlling for individual youth characteristics. In other words, within this study, race is a consistent factor that predicts the likelihood of recidivism. These findings are consistent with other scholarly work, with recent estimates showing that Black juveniles are more than 4 times as likely to be court-ordered to receive services within a secure-care setting than White juveniles, and Indigenous juveniles more than 3 times as likely to be court-ordered as White juveniles (Puzzanchera et al., 2015). Therefore, these results suggest that, while legal and policy reforms aimed at addressing practices within juvenile facilities and special education programs/related services within placements have recently pushed juvenile justice systems to have a greater focus on service delivery, rehabilitation, and transition/reentry for all juveniles, these reforms may fail to eliminate racial and ethnic disparities (Rovner, 2016).

Yet, even though there are some limited findings and patterns from this exploratory analysis, this study illustrates the need for cross-sector partnerships to support system-involved youth. That is, juveniles returning to their communities require multiple agencies to work in concert to prevent the reoccurrence of offenses. Indeed, Coburn and Penuel (2016) note the importance of collaboration across agencies (e.g., juvenile justice, social work, education) to share insights and evidence within an education context. Moreover, determining the possible extent and limitations of RPPs, understanding challenges with involving and informing juvenile justice professionals of evidence-based practices, and recognizing pitfalls associated with developing these partnerships to increase information dissemination of research collected within actual residential settings remain (Love & Harvell, 2016). Accordingly, the following section contextualizes the study’s findings and situates them into a larger discussion regarding RPP recommendations within the juvenile justice system based upon challenges found within these findings.

Offer Additional Placement Training Based Upon Evidence-Based Practices

Within this study, groups and programs with the lowest percentages of Black or Indigenous juveniles were also the groups or programs with the lowest rates of recidivism. From an RPP perspective, professionals working within secure-care setting placements must consider paying greater attention to how juveniles are grouped or placed after being sentenced to a court-ordered detainment. That is, there may be a need for practitioners to be trained to engage in intentional efforts to distribute program effects, treatment, and high-quality services across the juvenile population within secure-care settings in ways that are culturally responsive. Moreover, these types of exercises align with recent mandates under the 2018 JJDPA reauthorization, which requires that juvenile justice systems identify practices that result in racial disparities and systematically reduce these disparities (JJDPA, 1974).

For instance, professionals could be provided training on how to discuss appropriate and equitable program placements during intake, with team follow-up throughout a juvenile’s stay and upon community re-entry (Strassfeld, 2021). Moreover, specific training regarding the importance of heterogeneous group and program placement will allow practitioners working with juveniles in secure-care settings an opportunity to consider how to best use resources to provide preventive and rehabilitative strategies to all juveniles, particularly for racial or ethnic minority juveniles who are at higher risk for recidivism. In practice, these trainings could provide juvenile justice system professionals with case-based opportunities to examine profiles of youth like those in facilities where professionals work, and professionals could consider how to make placement decisions based upon individual, identified needs within a profile (Strassfeld, 2021). In addition, with training opportunities such as these, there is also the prospect of improving professionals’ understanding of which services, groups, and programs are most clearly linked to reduced recidivism.

Relatedly, as the research base for this type of intake training emerges, it is even more critical to advance high-quality research that sheds new insight on how placement decisions occur in practice and can be reformed in the future. Though the research is emerging, initial analysis signals that professionals continue to need significant training to adopt evidence-based practice and that staff need help “learn[ing] to integrate the innovation into their work processes” (Taxman et al., 2014, p. 186).

Link Transition and Reentry Support With Data and Research

Within this study, the percentage of African American and Indigenous formerly court-ordered juveniles who recidivated in comparison to their White counterparts, even after county effects were accounted for, suggests a need to design and implement programs that specifically assist African American and Indigenous court-ordered youth as they transition back into communities that may not be able to adequately receive them. In addition, across the research base, juveniles such as those within this study are reentering communities that are often plagued with excessive surveillance and racial profiling (Forman, 2017), substandard housing (Myers & Farrell, 2008), inadequate education systems (Katsiyannis et al., 2008), and a dearth of employment opportunities (Myers & Farrell, 2008). Upon return, they are also more likely to be served across a spectrum of various social services and public agencies including schools, mental health agencies, or child welfare and health agencies (Baglivio et al., 2014; Myers & Farrell, 2008). This speaks to negative system exposure and inequities for an at-risk population (and their families) who, due to these contextual factors, are at greater risk for recidivism.

From an RPP perspective, it is imperative that intervention research with a multi-systemic and integrative approach be used to identify and address various needs as youth transition back into their communities. Additional research may be needed to address how juvenile justice system professionals can support the youth’s reentry into an inequitable society that disadvantages them and their families. For example, in a study comparing a sample of juvenile offenders (n = 63 reentry program juveniles and 49 juveniles who received standard supervised probation), indicators suggested that reentry services were associated with greater access to ongoing rehabilitation and social services and a reduced likelihood to test positive for drugs or to recidivate (Bouffard & Bergseth, 2008). An emerging model such as Project SUPPORT (Service Utilization to Promote the Positive Rehabilitation and Community Transition of Incarcerated Youth with disabilities) has been identified as an evidence-based practice to guide juvenile justice system professionals as they support a juvenile’s reentry into education, community, and employment programs (Unruh et al., 2009). Specifically, the population for Project SUPPORT comprised incarcerated youth with either/both a special education disability or psychiatric diagnosis. Within the intervention, youth were provided with individualized, pre-release transition support and education, self-determination and social skills training, and youth had access to a collaborative transition team led by a transition specialist (Unruh et al., 2009). As systems consider updating practices in real time, it may be necessary to add to the emerging research in this area and provide data regarding adoption of these types of specific practices.

Disseminate Research With Stronger Findings and More Robust Reporting Indices

As evidenced from this exploratory analysis, the dataset did not include information regarding intervention fidelity for programs or groups where juveniles were placed, as these data were not collected by the agency itself. When working with datasets collected by juvenile justice system facilities, this is a common limitation, as “[fidelity] in the execution of interventions and the need for more systematic analysis of the process of intervention delivery are not challenges new to [juvenile justice]” (Evans-Chase & Zhou, 2014, p. 456). Accordingly, from an RPP perspective, it is imperative that future research in this challenging area marry information regarding fidelity with stronger findings and other pertinent study data. With this, it becomes possible to build reporting indices and/or juvenile justice reporting clearinghouses such as the What Works Clearinghouse (which reviews research across a variety of research topics including children and youth with disabilities or postsecondary outcomes) to determine which practices or programs best work for juvenile justice system professionals (Institute of Education Sciences, 2020).

In addition, this exploratory analysis highlighted challenges with interpreting placement data not collected by researchers. In this exploratory analysis, youth were assigned into programs and groups based on their individualized needs, but the dataset did not include intake notes regarding the significance of determinations and how these determinations are made. As prior work has found that juvenile sentencing may be racially biased (Engen et al., 2002; Moore & Padavic, 2010), it is conceivable that youth may not have been assigned to programs based solely on their needs. That is, there might have been considerable selection bias into these programs. Future RPP work should focus on how youth, particularly youth with ED or related conditions, are assigned to these programs, and as data become more available, the impacts of these programs can incorporate the selection biases into the programs in the first place.

Limitations

As previously mentioned, this exploratory analysis has limitations, some of which have been analyzed in context to how future RPP research can blend high-quality evidence-based research with targeted training for juvenile justice system professionals. In addition, there are other study limitations. The “county” variable was used as a proxy for neighborhood characteristics and socioeconomic status. More specifically, individual-level indicators of socioeconomic status (e.g., family income level, family debt-to-income ratio) were not included within our analyses of the Minnesota dataset because intake coordinators did not systematically collect this information for each juvenile. This limitation is common within studies of juveniles within the juvenile justice system, as key demographic information (e.g., parents’ educational attainment, family income, parents’ criminal record) is rarely collected systematically (Griller Clark & Unruh, 2010).

Second, criminal offenses are not provided at the individual level for criminal offense classification (e.g., felony, misdemeanor, or infraction status). Similarly, disability status for juveniles is not disaggregated beyond the designation of having an ED or related condition (e.g., anxiety disorders, behavioral/conduct disorders, psychotic disorders [Center for Parent Information & Resources, 2019]). These are also common limitations, as juvenile justice data are rarely linked to adult or juvenile recidivism data, let alone linked to ED status or criminal offense data at the individual level (Griller Clark & Unruh, 2010). Moreover, pertinent information for juveniles’ files are infrequently updated, and juveniles’ files are often not transferred completely when juveniles move from placement to placement or to a home school upon community reentry (Griller Clark & Unruh, 2010). Future research is needed to examine pertinent individual-level demographic information, and it is critical that mandated information collection practices be enforced.

Finally, the present exploratory analysis examination did not include comparison data for other agencies contracted out by the State to provide education or behavioral and mental health services to juveniles in secure-care settings, nor did it include analysis comparing the outcomes of juveniles in secure-care settings to those in other types of residential placements (e.g., juvenile boot camps) or adult correctional facilities. Again, these data are rarely systematically collected and agencies can be hesitant to share data with researchers, but it remains critically important for future research to, when possible, compare youth with ED in court-ordered secure-care settings to their comparable peers within other parts of the juvenile justice system (Zhang et al., 2011). From this exploratory analysis, it is also clear that those interpreting juvenile justice data and analysis should consider the data collection limitations of agencies and providers within the juvenile justice system, particularly because intake or transition coordinators are often not trained in data collection and/or only have access to a particular component of a youth’s sealed court record (Leone & Weinberg, 2012; Strassfeld, 2021).

Conclusion

Within this examination of recidivism patterns for formerly court-ordered juveniles with identified ED or related conditions, findings indicate consistently poorer outcomes for Black and Indigenous juveniles. Descriptively, programs, which are based on individual needs, have considerably different racial compositions within this analysis of Minnesota administrative data. Moreover, there remains a need to examine further the individual-level risk factors for juveniles with ED or related conditions and any associations with recidivism. In addition, with the goal of reducing recidivism as youth re-enter communities, it may be necessary to strengthen legally and policy-mandated program and service delivery with evidence-based practices and partnerships that increase access to services delivered by juvenile justice system professionals, equity, and reentry and transition service opportunities for juveniles.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Natasha M. Strassfeld

Notes

References

Baglivio

M. T.

Epps

Swartz

Huq

M. S.

Sheer

Hardt

N. S.

(2014). The prevalence of adverse childhood experiences (ACE) in the lives of juvenile offenders. Journal of Juvenile Justice, 3(2), 1–23.

Baglivio

M. T.

Wolff

K. T.

Jackowski

Chapman

Greenwald

M. A.

Gomez

(2018). Does treatment quality matter? Multilevel examination of the effects of intervention quality on recidivism of adolescents completing long-term juvenile justice residential placement. Criminology and Public Policy, 17(1), 147–180.

Barrett

D. E.

Katsiyannis

(2015). Juvenile delinquency recidivism: Are black and white youth vulnerable to the same risk factors? Behavioral Disorders, 40(3), 184–195. http://www.jstor.org/stable/43740446

Barrett

D. E.

Katsiyannis

Zhang

(2010). Predictors of offense severity, adjudication, incarceration, and repeat referrals for juvenile offenders: A multicohort replication study. Remedial and Special Education, 31(4), 261–275. https://doi.org/10.1177/0741932509355990

Becker

S. P.

Kerig

P. A.

Lim

Ezechuku

R. N.

(2012). Predictors of recidivism among delinquent youth: Interrelations among ethnicity, gender, age, mental health problems, and posttraumatic stress. Journal of Child & Adolescent Trauma, 5, 145–160. https://doi.org/10.1080/19361521.2012.671798

Behrens

J. T.

(1997). Principles and procedures of exploratory data analysis. Psychological Methods, 2(2), 131–160.

Behrens

J. T.

(2003). Exploratory data analysis. In Schinka

J. A.

Velicer

W. F.

(Eds.), Handbook of psychology: Research methods in psychology (pp. 33–64). John Wiley.

Blomberg

T. G.

Bales

W. D.

Mann

Piquero

A. R.

Berk

R. A.

(2011). Incarceration, education, and transition from delinquency. Journal of Criminal Justice, 39(4), 355–365. https://doi.org/10.1016/j.jcrimjus.2011.04.003

Bouffard

J. A.

Bergseth

K. J.

(2008). The impact of reentry services on juvenile offenders’ recidivism. Youth Violence and Juvenile Justice, 6(3), 295–318. https://doi.org/10.1177/1541204007313384

10.

Bullis

Yovanoff

Mueller

Havel

(2002). Life on the “outs”—Examination of the facility-to-community transition of incarcerated youth. Exceptional Children, 69(1), 7–22. https://doi.org/10.1177/001440290206900101

11.

Campbell

N. A.

Barnes

A. R.

Mandalari

Onifade

Campbell

C. A.

Anderson

V. R.

Kashy

D. A.

Davidson

W. S.

(2018). Disproportionate minority contact in the juvenile justice system: An investigation of ethnic disparity in program referral at disposition. Journal of Ethnicity in Criminal Justice, 16(2), 77–98. https://doi.org/10.1080/15377938.2017.1347544

12.

Center for Parent Information & Resources. (2019). Emotional disturbance. https://www.parentcenterhub.org/emotionaldisturbance/

13.

Coburn

C. E.

Penuel

W. R.

(2016). Research practice partnerships in education: Outcomes, dynamics, and open questions. Educational Researcher, 45(1), 48–54. https://doi.org/10.3102/0013189X16631750

14.

Engen

R. L.

Steen

Bridges

G. S.

(2002). Racial disparities in the punishment of youth: A theoretical and empirical assessment of the literature. Social Problems, 49(2), 194–220. https://doi.org/10.1525/sp.2002.49.2.194

15.

Evans-Chase

Zhou

(2014). A systematic review of the juvenile justice intervention literature: What it can (and cannot) tell us about what works with delinquent youth. Crime and Delinquency, 60(3), 451–470. https://doi.org/10.1177/0011128712466931

16.

Every Student Succeeds Act, 20 U.S.C. § 6301 et seq. (2015).

17.

Fazel

Doll

Långström

(2008). Mental disorders among adolescents in juvenile detention and correctional facilities: A systematic review and metaregression analysis of 25 surveys. Journal of the American Academy of Child and Adolescent Psychiatry, 47(9), 1010–1019. https://doi.org/10.1097/CHI.0b013e3187eecf3

18.

Forman

Jr. (2017). Locking up our own: Crime and punishment in Black America. Farrar, Straus and Giroux.

19.

Gage

N. A.

Josephs

N. L.

Lunde

(2012). Girls with emotional disturbance and a history of arrest: Characteristics and school-based predictors of arrest. Education and Treatment of Children, 35(4), 603–622. https://www-jstor-org-443.web.bisu.edu.cn/stable/42900177

20.

Gagnon

J. C.

Barber

(2010). Characteristics of and services provided to youth in secure care facilities. Behavioral Disorders, 36(1), 7–19. http://www.jstor.org/stable/43153826

21.

Gagnon

J. C.

Read

N. W.

Gonsoulin

(2015). Key considerations in providing a free appropriate public education for youth with disabilities in juvenile justice secure care facilities. https://files.eric.ed.gov/fulltext/ED571826.pdf

22.

Griller Clark

Unruh

. (2010). Transition practices for adjudicated youth with E/BDs and related disabilities. Behavioral Disorders, 36(1), 43–51. http://www.jstor.org/stable/43153829

23.

Grunwald

H. E.

Lockwood

Harris

P. W.

Mennis

(2010). Influences of neighborhood context, individual history and parenting behavior on recidivism among juvenile offenders. Journal of Youth Adolescence, 39, 1067–1079. https://doi.org/10.1007/s10964-010-9518-5

24.

Hoeve

McReynolds

L. S.

Wasserman

G. A.

(2013). Service referral for juvenile justice youths: Associations with psychiatric disorder and recidivism. Administration and Policy in Mental Health and Mental Health Services Research, 41, 379–389. http://doi.org/10.1007/s10488-013-0472-x

25.

Individuals with Disabilities Education Improvement Act, 20 U.S.C. § 1400 et seq. (2004).

26.

Institute of Education Sciences. (2020). Welcome to the What Works Clearinghouse. https://ies.ed.gov/ncee/wwc/

27.

Juvenile Justice and Delinquency Prevention Act, 42 U.S.C. § 5601 et seq. (1974).

28.

Katsiyannis

Ryan

J. B.

Zhang

Spann

(2008). Juvenile delinquency and recidivism: The impact of academic achievement. Reading & Writing Quarterly, 24(2), 177–196. https://doi.org/10.1080/10573560701808460

29.

Kumm

Maggin

Brown

Talbott

(2019). A meta-analytic review of mental health interventions targeting youth with internalizing disorders in juvenile justice facilities. Residential Treatment for Children & Youth, 36(3), 235–256. https://doi.org/10.1080/0886571X.2018.1560716

30.

Lane

Goldstein

E. S.

Heilbrun

Cruise

K. R.

Pennacchia

(2012). Obstacles to research in residential juvenile justice facilities: Recommendations for researchers. Behavioral Sciences and the Law, 30, 49–68. https://doi.org/10.1002/bsl.1991

31.

Leiber

M. J.

Mack

K. Y.

(2003). The individual and joint effects of race, gender, and family status on juvenile justice decision-making. Journal of Research in Crime and Delinquency, 40(1), 34–70. https://doi.org/10.1177/0022427802239253

32.

Leone

P. E.

Meisel

S. M.

Drakeford

(2002). Special education programs for youth with disabilities in juvenile corrections. Journal of Correctional Education, 53(2), 46–50. http://www.jstor.org/stable/41971066

33.

Leone

P. E.

Weinberg

(2010). Addressing the unmet educational needs of children and youth in the juvenile justice and child welfare systems. https://www.aecf.org/resources/addressing-the-unmet-educational-needs

34.

Leve

L. D.

Chamberlain

(2005). Associations with delinquent peers: Intervention effects for youth in the juvenile justice system. Journal of Abnormal Child Psychology, 33(3), 339–347. https://doi.org/10.1007/s10802-005-3571-7

35.

Love

Harvell

(2016). Understanding research and practice gaps in juvenile justice: Early insights from the Bridge Project. Office of Juvenile Justice and Delinquency Prevention. https://www.ncjrs.gov/pdffiles1/ojjdp/grants/250489.pdf

36.

Lyons

J. S.

Baerger

D. R.

Quigley

Erlich

Griffin

(2001). Mental health service needs of juvenile offenders: A comparison of detention, incarceration, and treatment settings. Children’s Services: Social Policy, Research, and Practice, 4(2), 69–85. https://doi.org/10.1207/S15326918CS0402_2

37.

Maxfield

L. D.

(2005). Measuring recidivism under the federal sentencing guidelines. Federal Sentencing Reporter, 17(3), 166–170. http://www.jstor.org/stable/10.1525/fsr.2005.17.3.166

38.

Moore

L. D.

Padavic

(2010). Racial and ethnic disparities in girls’ sentencing in the juvenile justice system. Feminist Criminology, 5(3), 263–285. https://doi.org/10.1177/1557085110380583

39.

Myers

D. M.

Farrell

A. F.

(2008). Reclaiming lost opportunities: Applying public health models in juvenile justice. Children and Youth Services Review, 30(10), 1159–1177. https://doi.org/10.1016/j.childyouth.2008.03.002

40.

National Center for Mental Health and Juvenile Justice. (2013). Better solutions for youth with mental health needs in the juvenile justice system. https://ncyoj.policyresearchinc.org/img/resources/Better_Solutions_for_Youth_with_Mental_Health_Needs_in_the_Juvenile_Justice_System-501172.pdf

41.

I. Y. H.

(2010). Where juvenile serious offenders live: A neighborhood analysis of Wayne County, Michigan. Journal of Criminal Justice, 38(2), 207–215. https://doi.org/10.1016/j.jcrimjus.2010.02.002

42.

Prison Policy Initiative. (2019). Youth confinement: The whole pie 2019. https://www.prisonpolicy.org/reports/youth2019.html

43.

Puzzanchera

Sladky

Kang

(2015). Easy access to juvenile populations: 1990–2014. http://www.ojjdp.gov/ojstatbb/ezapop

44.

Quinn

M. M.

Rutherford

R. B.

Leone

P. E.

Osher

D. M.

Poirier

J. M.

(2005). Youth with disabilities in juvenile corrections: A national survey. Exceptional Children, 71(3), 339–345. https://doi.org/10.1177/001440290507100308

45.

Rovner

(2016). Policy brief: Racial disparities in youth commitments and arrests. https://www.sentencingproject.org/wp-content/uploads/2016/04/Racial-Disparities-in-Youth-Commitments-and-Arrests.pdf

46.

Sinclair

J. G.

Unruh

D. K.

Griller Clark

Waintrup

M. G.

(2017). School personnel perceptions of youth with disabilities returning to high schools from the juvenile justice system. The Journal of Special Education, 51(2), 95–105. https://doi.org/10.1177/0022466916676089

47.

Stone

Zibulsky

(2015). Maltreatment, academic difficulty, and systems-involved youth: Current evidence and opportunities. Psychology in the Schools, 52(1), 22–39. https://doi.org/10.1002/pits.21812

48.

Strassfeld

N. M.

(2021). Transition for system-involved youth with disabilities: Law, compliance, and support. Education and Treatment of Children, 44(1), 37–47. https://doi.org/10.1007/s43494-021-00036-4

49.

Swank

J. M.

Gagnon

J. C.

(2016). Mental health services in juvenile correctional facilities: A national survey of clinical staff. Journal of Child and Family Studies, 25(9), 2867–2872. https://doi.org/10.1007/s10826-016-0436-3

50.

Swank

J. M.

Gagnon

J. C.

(2017). A national survey of mental health screening and assessment practices in juvenile correctional facilities. Child & Youth Care Forum, 46, 379–393. https://doi.org/10.1007/s10566-016-9379-5

51.

Swayze

Buskovick

(2012). Minnesota juvenile diversion: A summary of statewide practices and programming. Minnesota Department of Public Safety Office of Justice Programs. https://dps.mn.gov/divisions/ojp/forms-documents/Documents/Juvenile%20Justice%20Reports/MINNESOTA%20JUV_DIV%20REPORT_Final.pdf

52.

Taxman

F. S.

Henderson

Young

Farrell

(2014). The impact of training interventions on organizational readiness to support innovations in juvenile justice offices. Administration and Policy in Mental Health, 41, 177–188. https://doi.org/10.1007/s10488-012-0445-5

53.

Thompson

K.C.

Morris

R. J.

(2016). Juvenile Delinquency and Disability (1st ed.). Springer.

54.

Unruh

D. K.

Gau

J. M.

Waintrup

M. G.

(2009). An exploration of factors reducing recidivism rates of formerly incarcerated youth with disabilities participating in a re-entry intervention. Journal of Child and Family Studies, 18, 284–293. https://doi.org/10.1007/s10826-008-9228-8

55.

U.S. Departments of Education & Justice. (2014). Guiding principles for providing high-quality education in juvenile justice secure care settings. https://www2.ed.gov/policy/gen/guid/correctional-education/guiding-principles.pdf

56.

Zhang

Hsu

Katsiyannis

Barrett

D. E.

(2011). Adolescents with disabilities in the juvenile justice system: Patterns of recidivism. Exceptional Children, 77(3), 283–298. https://doi.org/10.1177/001440291107700302