Problem-Solving Courts and Mental Health in Criminal Justice

Introduction and Background

The National Alliance on Mental Illness (NAMI) has raised concern about the state of mental health in the United States and the nation's role in perpetuating the criminalization of people with mental illness. Recent data from NAMI indicates that 1 in every 5 adults in the U.S. (representing 21 percent of the population) suffered from mental illness in 2020 (NAMI, 2021). Of these, 27 percent — translating to 14.2 million people — suffered from serious mental illness (NAMI, 2021). Unfortunately, people with mental illness are overrepresented in the criminal justice system. Around 2 million people with serious mental illness are booked into American jails every year, and 2 in every 5 people who are incarcerated in the U.S. report having a history of mental illness (NAMI, 2021).

The National Commission on Correctional Healthcare reports that between 13 and 18 percent of inmates in American prisons have major depression, between 2 and 4 percent have bipolar disorder, and around 3.9 percent suffer from schizophrenia and other psychotic disorders (Agency for Healthcare Research and Quality, 2012). Unfortunately, only 45 percent of people with serious mental illness in local jails and 37 percent of those in federal and state prisons receive treatment while incarcerated (NAMI, 2021). In the juvenile system, an estimated 70 percent of offenders suffer from a mental health condition, and data indicate that juveniles in detention are ten times more likely than the general youth population to suffer from psychosis (NAMI, 2021).

Prisons and jails are not structured or financed to offer effective mental health services. As a result, offenders with mental illness are more likely to be held in solitary confinement and more likely to commit suicide while in prison (NAMI, 2021). Furthermore, untreated mental illness increases one's risk of recidivism upon release (Zgoba et al., 2022). This warrants solutions aimed at enhancing the criminal justice system's support for offenders with mental illness.

An intervention that has emerged as an innovative effort in this area is the problem-solving court, which focuses on using therapeutic techniques to address mental health and substance use disorders among offenders. This research analyzes the factors influencing the effectiveness of problem-solving courts in instilling positive behavior and minimizing the risk of recidivism among at-risk offenders.

Mental illness — a condition affecting a person's mood, behavior, or thinking, which affects their ability to relate effectively with others and interferes with daily living (NAMI, 2021).

In-jail mental health treatment programs — the range of psychological and pharmacological interventions offered to offenders during incarceration to treat mental illness and help them transition into the community.

Problem-solving court — a court within the judicial system that uses therapeutic techniques to reduce the risk of recidivism among offenders with mental health and substance abuse disorders.

BJS — Bureau of Justice Statistics. CJS — criminal justice system. ICPSR — Inter-University Consortium for Political and Social Research.

Studies have shown that mentally ill offenders are at a higher risk of reoffending, failure to comply with parole requirements, and re-incarceration upon release from prison (Zgoba et al., 2022). At the same time, evidence shows that offenders who received treatment for their mental illness while incarcerated were less likely to commit serious crimes upon re-entry into the community (Zgoba et al., 2022). One of the roles of the criminal justice system is to facilitate the successful reintegration of offenders into the community (Zgoba et al., 2022). Untreated or poorly managed mental illness increases the risk of recidivism, thus interfering with this role. This study seeks to assess the effectiveness of problem-solving courts as a mental health intervention in the U.S. criminal justice system. It is guided by three research questions:

(i) What is the relationship between access to inpatient mental health treatment and behavioral change among mentally ill inmates in problem-solving courts in the U.S.?

(ii) What is the relationship between outpatient mental health treatment and behavioral change among mentally ill inmates in problem-solving courts in the U.S.?

(iii) What is the relationship between the frequency of court sessions and behavioral change among mentally ill inmates in problem-solving courts in the U.S.?

Literature Review: Mental Health in the Criminal Justice System

In their study, Bandara et al. (2018) measured the attitudes of community mental health providers toward clients involved with the criminal justice system. They issued a survey to 627 mental health clinical providers from psychiatric rehabilitation programs in Maryland. Measures evaluated how well-versed clinicians were in dealing with, valuing, and perceiving similarities among their clients with significant mental illness. The authors used chi-square test analysis to compare these results with those of providers who did not work with justice-involved clients.

Compared to clients without criminal justice involvement, providers indicated less respect for clients with such involvement — 79 percent lower respect for non-criminal clients versus 95 percent lower for criminal clients. When asked to assess their similarity with clients, fewer than 50 percent suggested they were similar to justice-involved clients.

At the same time, the criminal justice system is comprised of individuals who have a higher prevalence of mental health issues (Bandara et al., 2018). Compared to those without major mental disorders, those with serious mental illnesses have disproportionately high rates of criminal justice involvement. Between 2008 and 2014, approximately 25 to 27 percent of people with mental illness reported at least one arrest during their lifetime, compared to 17 to 18 percent among those without mental illness.

Lamberti (2020) suggests this disparity is especially problematic because optimized mental health treatment programs in prison can reduce rates of recidivism. Criminal justice cooperation was described as a step-by-step process that integrates the best practices from each profession. Some strategies rely on "jail diversion," which includes formally implemented programs such as parole, probation, mental health courts, diversion programs, and conditional release. However, studies have shown that these approaches are relatively ineffective for offenders with severe mental illness (Lamberti, 2020). Strategies relying primarily on compliance and surveillance — such as parole and home detention — are typically ineffective. Lamberti (2020) recommends a collaborative process involving engagement, assessment, planning and treatment, monitoring, problem-solving, and transition.

A study by Timmer and Nowotny (2021) highlights multiple factors to consider when working with criminal justice clients who have mental health issues. A conceptual model accounts for predisposing and vulnerability-increasing factors, such as history of arrest, probation, or post-release supervision, as well as negative enabling factors, such as employment status, government program coverage, and poverty. Criminal justice systems must also attend to the availability of inpatient and outpatient treatment and medication (Timmer & Nowotny, 2021). The authors assert that an optimal mental health care program in a criminal justice facility would stress the need for several social institutions — including welfare agencies, research institutions, and community mental health organizations — to collaborate in serving vulnerable clients.

One significant challenge is that regular mental health providers may have limited knowledge about criminal justice clients. Hean et al. (2015) note that joint training and interprofessional, interdisciplinary education is largely absent from both healthcare education and professional development circles within criminal justice. Approximately 7 to 9 out of 10 people in the criminal justice system have a psychological disorder. Criminal justice clients fall on a spectrum between mental health services and criminal justice services (Hean et al., 2015), which sparks debate about increasing both capacity and awareness.

One strategy proposed by the authors is to increase collaboration between mental health services and criminal justice services. Professionals from the legal and mental health fields must possess interprofessional collaboration skills in order to work together successfully to achieve the diversion agenda and meet the needs of mentally ill offenders. For instance, a common complaint is that police officers lack knowledge about mental health. Participants in the authors' interviews suggested it was beneficial to bring "people together from across a wide geographical area to compare different and good practice" (Hean et al., 2015, p. 10). Raising awareness about collaborative approaches is thus instrumental for securing good practice.

Another key area involves differentiating between mental health in the criminal justice system and regular mental health care. According to Ghiasi et al. (2022), Antisocial Personality Disorder (ASPD) is frequently diagnosed in the prisoner population (Yousefi & Talib, 2022). However, it is important not to equate mental illness with criminal activity. Clinicians must ensure that diagnoses are applied only when the relevant criteria are genuinely present, in order to prevent offenders from using mental disorders as an excuse to avoid punishment. Yousefi and Talib (2022) note that Major Depressive Disorder, ASPD, and Borderline Personality Disorder are also quite common among incarcerated populations, based on a study conducted in Iran. It is therefore important for mental health professionals working in criminal justice settings to identify which disorders are more prevalent among the incarcerated population than among the general population.

A substantial body of literature exists on mental health in the criminal justice system; however, studies focused specifically on problem-solving courts remain limited. This study helps fill that gap by offering policymakers crucial insights on how to improve the effectiveness of problem-solving courts as a form of intervention for offenders with mental health issues. This is especially pertinent in the context of ongoing debates about redirecting funding toward mental health resources and services.

Methodology

The study uses quantitative secondary data obtained from the Census of Problem-Solving Courts commissioned by the Bureau of Justice Statistics in 2012. This section presents the sampling techniques, research instrument, and data collection processes.

The target population for this study is staff working in problem-solving courts in 2012. The study analyzes secondary data collected from the 2012 Census of Problem-Solving Courts, which is the most recent year for which data were available. The researcher received permission to use the data from the ICPSR after indicating that the study was for academic purposes. The census defined a problem-solving court as a program or docket within the judicial branch that relies on therapeutic justice to reduce recidivism among offenders with underlying social, mental health, and substance abuse problems (Census of Problem-Solving Courts, 2012).

Given the wide variety of diversion programs meeting this definition, the sampling procedure began with the identification of all diversion programs focused on using therapeutic justice to minimize the risk of recidivism. The study used a combination of convenience and snowball sampling to select programs for the final sample. To be eligible for inclusion, a diversion program had to operate within the judicial system, be under the supervision of a judicial officer, and be justice-involved — meaning it intervened only after a formal charge by the prosecutor (Census of Problem-Solving Courts, 2012). Convenience sampling was appropriate given the wide variety of diversion models and programs in existence and the lack of a standard definition for problem-solving courts. Youth courts operated as school-based or community-based programs, and those operating under a peer jury or youth judge model, were excluded (Census of Problem-Solving Courts, 2012). As part of snowball sampling, participating problem-solving courts were permitted to refer other eligible programs to take part in the study.

Data were collected through a web-based survey that measured the effectiveness of problem-solving courts using 371 variables assessing six fundamental areas: existence of a dedicated program or docket, ongoing judicial interactions between court and participants, partnership and collaboration between the court and other agencies, specialized team expertise, availability of therapeutic and rehabilitative services, and individualized treatment for participants.

This study selected four variables from the census to address the research questions. Variables were selected based on their relevance to the research questions, and the study assumes that the exclusion of other variables does not compromise validity and reliability. The study's primary interest was to measure the effectiveness of problem-solving courts in reducing problem behaviors that increase the risk of recidivism among offenders with mental health and substance abuse issues. The reduction in problem behaviors was measured by the proportion of total exits classified as successful (Census of Problem-Solving Courts, 2012). Successful exits are participants who complete the program and graduate, having met the defined criteria for good behavior. The variable "Successful Exits over Total Exits" in the dataset is the study's dependent variable. It is measured by a Likert scale, with responses ranging from 1 to 6, where 1 represents no successful exits and 6 represents 100 percent successful exits (Census of Problem-Solving Courts, 2012).

Since the focus is on mental health, the study concentrates on variables that measure the level of support offered to inmates with mental health issues. The independent variables selected measured access to inpatient and outpatient mental health treatment for offenders in problem-solving courts. Both independent variables were measured using Yes, No, and Don't Know responses. The study measured the significance of the association between the dependent variable and each independent variable. In addition, the researcher examined the relationship between the frequency of court sessions and the rate of successful completion. The frequency of court sessions variable was measured using a Likert scale with responses ranging from 1 to 5, where 1 represents daily sessions and 5 represents monthly sessions.

The study uses official data gathered during the 2012 Census on Problem-Solving Courts. The web-based survey was conducted between January 30, 2013, and January 31, 2014, with a response rate of 86 percent from eligible courts that were cleared to participate (Census of Problem-Solving Courts, 2012). The final sample comprised 3,633 problem-solving courts. All 3,633 courts received official invitations to participate, but only 3,131 submitted responses, with 502 declining to participate (Census of Problem-Solving Courts, 2012).

This study prefers secondary data because it provides a platform to study problem-solving courts across the country and to observe a large sample with minimal resources. Problem-solving courts vary by features across jurisdictions, and the use of questionnaires limited to a specific jurisdiction may produce findings that are not generalizable.

The data were analyzed using both descriptive and inferential statistics. Descriptive statistics were obtained for both demographic variables and the dependent variable. The chi-square test was used to assess whether the dependent and independent variables were independent of each other. Correlation analysis was then used to determine the strength and direction of the relationships between the dependent and independent variables.

Conclusion

Problem-solving courts have emerged as an innovative effort in addressing mental health and substance abuse problems among offenders in the criminal justice system. This quantitative study sought to determine the factors influencing the effectiveness of problem-solving courts in reducing recidivism among offenders with mental health issues. Using data from the 2012 Census on Problem-Solving Courts, the study analyzed the effect of three independent variables — access to inpatient mental health treatment, access to outpatient mental health treatment, and frequency of court sessions — using chi-square analysis in SPSS.

The study findings showed a positive correlation between the likelihood of successful graduation and all three independent variables. However, access to inpatient mental health treatment yielded a non-significant effect on behavioral change, while both access to outpatient treatment and frequency of sessions yielded statistically significant results. Therefore, the study concludes that problem-solving courts could improve their effectiveness in fostering positive behavior among offenders with mental health issues by increasing access to outpatient mental health treatment and scheduling sessions on a weekly basis.

The primary weakness of the study is its reliance on secondary data, which limits the ability to obtain first-hand accounts from the target population. In addition, the study captures only the perspectives of contact persons working in problem-solving courts, disregarding the opinions of the offenders whom these programs serve. Finally, the study limits itself to correlation analysis, which does not indicate causality or the extent to which the independent variables predict changes in the dependent variable (Pearl et al., 2016).

Future studies could replicate the study using primary data collected through interviews or surveys administered directly to offenders in problem-solving courts to compare findings. Studies could also expand the scope to include regression analysis to determine how much of the variance in the dependent variable is explained by the selected independent variables (Pearl et al., 2016).