Ethical and Legal Implications Reducing Medication Errors

Factors Influencing the Effectiveness of Problem-Solving Courts in Addressing Mental Health Issues within the Criminal Justice System

The National Alliance on Mental Health (NAMI) has raised concern about the state of mental health in the United States (US) 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 US (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 US 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 diseases (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 in jail (NAMI, 2021). In the juvenile system, an estimated 70 percent of offenders suffer from a mental health condition, with data indicating 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. Hence, offenders with mental illness are more likely to be held in solitary confinement and more likely to commit suicide while in prison (NAMI, 2021). Further, 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 supporting mental health in the criminal justice system (CJS) is problem-solving courts, which focus 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 behaviour and minimizing the risk of recidivism among at-risk offenders.

Definition of Terms and Abbreviations

i) Mental illness – a condition affecting a person’s mood, behaviour, or thinking, which affects their ability to relate effectively with others and interferes with daily living (NAMI, 2021b).

ii) 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.

iii) 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.

iv) BJS – Bureau of Justice Statistics

v) CJS – criminal justice system

vi) ICPSR - Inter-University Consortium for Political and Social Research

Purpose of the Study

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 US 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 US?

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

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

Literature Review

Mental Health Concerns in the CJS

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

Compared to clients without such participation in the criminal justice system, providers indicated less respect for such clients. They had lower respect for criminal clients (79% lower for non-criminal and 95% lower for criminal). When asked to offer their opinion about similarity with clients, under 50% suggested they were similar to the criminal justice-embedded 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 participation in the criminal judicial system. Between the years 2008 and 2014, about 25 – 27% of those with mental illness had reported at least one arrest during their lifetime, while without mental illness, the rate was between 17% and 18%.

Lamberti (2020) suggests this is problematic, considering that optimized and planned mental health treatment programs in prison can reduce rates of recidivism. There are various strategies that experts already utilize. Criminal justice cooperation was seen as a step-by-step process that integrates the best practices from each profession in Lamberti's (2020) article. Some strategies simply maintain ‘jail diversion,’ which means they are formally implemented and can include parole and probation, mental health court, diversion and conditional release programs. However, studies have shown that these are relatively ineffective for criminals with severe mental illness (Lamberti, 2020). Strategies relying on compliance and surveillance, such as parole and home detention, are typically ineffective. The authors suggest a collaborative process that involves engagement, assessment, planning and treatment, monitoring, problem-solving and transition.

Furthermore, a study by Timmer & Nowotny (2021) suggests there are multiple factors to consider when working with CJ (criminal justice) clients with mental health issues. A conceptual model considers features like predisposing and vulnerability-increasing factors, such as history of arrest, probation, or supervision post-release and negative enabling factors, like employment status, government programs, coverage and poverty. CJ systems must also inpatient/outpatient treatment or medication (Timmer & Nowotny, 2021). The authors assert that an optimal mental health care program in a CJ facility would stress the need for several social institutions (welfare, research, community mental health, etc.) to work together to serve vulnerable clients.

One of the issues with mental health services in general is that there may be low knowledge among regular mental health providers about CJ clients or offenders. At the same time, Hean et al (2015) note that joint training and interprofessional, interdisciplinary education in healthcare education is missing. It also rarely occurs in professional development circles for criminal justice. At the same time, approximately 7 to 9 out of 10 people in the CJ system have a psychological disorder. CJ clients fall on a spectrum between MHS (Mental Health Services) and CJS (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 MHS and CJS. Professionals from the legal and mental health fields must possess interprofessional collaboration skills in order to work together successfully to achieve the liaison and diversion agenda and to meet the requirements of mentally ill criminals. For instance, a common complaint is that police officers lack knowledge about mental health. Participants from the authors’ interviews suggested it was a good idea to bring “people together from across a wide geographical area to compare different and good practice” (Hean et al, 2015, p. 10). In other words, raising awareness about collaborative approaches is instrumental for securing good practice.

Another key area that will become important for the study is differentiating between mental health in the CJ system and regular mental health care. According to Ghiasi et al (2022), ASPD (Antisocial Personality Disorder) is frequently diagnosed in the prisoner population (Yousefi & Talib, 2022). However, it is important not to equate mental illness with criminal activity. Clinicians must make sure that diagnoses are used only when certain qualities are present in order to prevent offenders from using mental disorders as an excuse to avoid punishment. Yousefi & Talib (2022) note Major Depressive Disorder, ASPD and Borderline are also quite common, based on a study in Iran. It is thus important for mental health professionals working in CJ to identify which disorders are more common among the incarcerated population than among the non-incarcerated population.

A lot of literature exists on the area of mental health in the CJS. However, studies focused specifically on problem-solving courts are limited. The study will fill a gap in literature because it will offer policymakers crucial insights on how to improve the effectiveness of problem-solving courts as a form of intervention for offenders with mental health issues. I believe that this will go a long way towards enhancing these programs’ cost-effectiveness. This is especially important considering the current debates about defunding police, or more accurately, channeling funding into mental health resources and services.

Methodology

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

Sampling Techniques

The target population for this study is staff working in problem-solving courts in 2012. The study analyses secondary data collected from the 2012 Census of Problem-Solving Courts, which is the latest year for which data is 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 that meet the given 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 diversion programs into the final sample. To be eligible for inclusion, the diversion program had to operate within the judicial system, be under the supervision of a judicial officer, and be justice-involved, which meant that it only intervened after a charge by the prosecutor (Census of Problem-Solving Courts, 2012). Convenience sampling was appropriate in selecting programs into the sample 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 were excluded from the study just like those operating under a peer jury or youth judge model (Census of Problem-Solving Courts, 2012). As part of snowball sampling, participating problem-solving courts were allowed to refer other eligible programs to take part in the study.

Research Instrument

Data was 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/docket, ongoing judicial interactions between court and participants, partnership and collaboration between court and other agencies, specialized team expertise, availability of therapeutic/rehabilitative services, and individualized treatment for participants.

This study selected four variables from the census to inform the research questions. Variables were selected based on their relevance to the research questions and the study assumes that the exclusion of some of the variables in analysis does not compromise validity and reliability. The study’s main 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 that categorized 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 data set measures successful exists as a proportion of total exits and 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 in the data set that measure the level of support offered to inmates with mental health issues. As such, 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 of the independent variables. At the same time, the researcher studied the relationship between the frequency of court sessions and the rate of successful completion to determine the strength and significance of the association. The frequency of court sessions variable was measured using a Likert scale with responses ranging from 1 to 5, where 1 represents daily and 5 represents monthly sessions.

Data Collection Process

As mentioned previously, the study uses official data gathered during the 2012 Census on Problem-Solving courts. The survey, which was web-based, was carried out between 30th January 2013 and 31st January, 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 was made up of 3,633 problem-solving courts. The 3,633 courts received official invitations to participate, but only 3,131 submitted their responses, with 502 declining to participate (Census of Problem-Solving Courts, 2012).

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

The data was analysed 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 test whether the dependent and independent variables were independent of each other. Finally, correlation analysis was used to determine the strength and direction of the relationship between the dependent and independent variables.

Findings of the Study and Data Analysis

Descriptive Result of the Independent Variables

Table 1a. Descriptive Result of Access to Inpatient Mental Health Treatment

A total of 2,908 responses were received in the variable access to inpatient mental health treatment. Of these, 2,143 participants (73.7 percent) reported that their problem court does not offer inpatient mental health treatment, while 765 (26.3 percent) reported that their problem court offers inpatient mental health treatment. There were 725 missing responses, where participants either failed to respond to the question or gave a ‘don’t know’ response.

ORIGINAL_INPATIENT MENTAL HEALTH TREATMENT: TYPES OF SERVICES COMMONLY USED BY ACTIVE PARTICIPANTS IN YOUR COURT

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No

Yes

Total

Missing

Missing

Total

(Source: SPSS Output, Census of Problem-Solving Courts data set, 2012)

Table 1b: Descriptive Result of Access to Outpatient Mental Health Treatment

A total of 2,908 responses were received in regard to the variable access to outpatient mental health treatment. Of these, 1,244 participants (42.8 percent) reported that their problem court does not offer outpatient mental health treatment, while 1,664 (57.2 percent) reported that their problem court offers outpatient mental health treatment. There were 725 missing responses, where participants either failed to respond to the question or gave a ‘don’t know’ response.

ORIGINAL_OUTPATIENT MENTAL HEALTH TREATMENT: TYPES OF SERVICES COMMONLY USED BY ACTIVE PARTICIPANTS IN YOUR COURT

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No

Yes

Total

Missing

Missing

Total

(Source: SPSS Output, Census of Problem-Solving Courts data set, 2012)

Table 1c: Descriptive Result of Frequency of Court Sessions

This variable sought to measure the frequency of problem-solving court sessions. A total of 2,945 respondents (81.8 percent) responded to this question, while 688 (18.9 percent) either did not answer or responded with ‘Don’t know’. A majority (50.2 percent) of respondents indicated that their courts held weekly sessions, while 23 percent indicated that sessions were held less than weekly but more than once a month. Daily sessions were the least common at 4.2 percent. Only 6 percent of courts held sessions more than once a week, but less than daily, and only 7.6 percent had monthly sessions.

O_HOW FREQUENTLY IS COURT IN SESSION

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Daily

More than once a week but less than daily

Weekly

More than once a month but less than weekly

Monthly

Other

Total

Missing

Missing

Blank

Total

Total

Figure 1a: Pie Chart Indicating the Frequency of Court Sessions

Descriptive Result of the Dependent Variable

Table 2: Descriptive Result of Successful Exits Divided by Total Exits

The study’s focus is to assess the effectiveness of problem-solving courts in driving behavioral change among offenders and consequently, reducing their risk of recidivism. The variable ‘Successful exits divided by total exits’ gives a view of the proportion of offenders who successfully graduate from problem-solving courts and was thus used as a proxy for successful behavioral change. As table 2 below indicates, 270 courts of the 2,337 that responded (representing 11.6 percent) reported no exits or no successful exits in 2012. 30.9 percent of courts reported a successful exit rate of between 51 and 75 percent, while 28.2 percent reported that between 26 and 50 percent of exits were successful. 16.3 percent (381 courts) reported 78 to 99 percent successful exits, and only 7.7 percent reported 100 percent successful exits. A majority of problem-solving courts report a successful exit rate of between 26 and 99 percent.

SUCCESSFUL EXITS DIVIDED BY TOTAL EXITS, CATEGORIZED

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

No exits (0 divided by 0)

No successful exits

1-25% of exits successful

26-50% of exits successful

51-75% of exits successful

76-99% of exits successful

100% successful exits

Total

Missing

Missing or out of scope

Total

(Source: SPSS Output, Census of Problem-Solving Courts data set, 2012)

Crosstabs and Chi-Square Results

Table 3: Relationship between Access to Inpatient Mental Health Treatment and Behavioral Change as Measured by Successful Exits

ORIGINAL_INPATIENT MENTAL HEALTH TREATMENT: TYPES OF SERVICES COMMONLY USED BY ACTIVE PARTICIPANTS IN YOUR COURT * SUCCESSFUL EXITS DIVIDED BY TOTAL EXITS, CATEGORIZED Crosstabulation

Count

ORIGINAL_INPATIENT MENTAL HEALTH TREATMENT: TYPES OF SERVICES COMMONLY USED BY ACTIVE PARTICIPANTS IN YOUR COURT

Total

No

Yes

SUCCESSFUL EXITS DIVIDED BY TOTAL EXITS, CATEGORIZED

No exits (0 divided by 0)

No successful exits

1-25% of exits successful

26-50% of exits successful

51-75% of exits successful

76-99% of exits successful

100% successful exits

Total

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

8.766a

Likelihood Ratio

Linear-by-Linear Association

N of Valid Cases

a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 25.14.

The case processing summary indicates a total of 2,309 valid responses and 1,324 missing responses. The null hypothesis and alternative hypotheses are as stated below:

Ho: there is no significant association between access to inpatient mental health treatment and successful exit in problem-solving courts

Ha: There is a significant association between access to inpatient mental health treatment and successful exit in problem-solving courts

The Pearson chi-square value of 8.766 shows that the two variables are positively correlated. However, the p-value (p = 0.187) is greater than the 0.05 significance level, indicating that the correlation is not significant. Hence, we accept the null hypothesis and conclude that although access to inpatient mental health increases the likelihood of successful exit, the association between the two variables is weak.

Table 4: Relationship between Access to Outpatient Mental Health Treatment and Behavioral Change as Measured by Successful Exits

ORIGINAL_OUTPATIENT MENTAL HEALTH TREATMENT: TYPES OF SERVICES COMMONLY USED BY ACTIVE PARTICIPANTS IN YOUR COURT * SUCCESSFUL EXITS DIVIDED BY TOTAL EXITS, CATEGORIZED Crosstabulation

Count

ORIGINAL_OUTPATIENT MENTAL HEALTH TREATMENT: TYPES OF SERVICES COMMONLY USED BY ACTIVE PARTICIPANTS IN YOUR COURT

Total

No

Yes

SUCCESSFUL EXITS DIVIDED BY TOTAL EXITS, CATEGORIZED

No exits (0 divided by 0)

No successful exits

1-25% of exits successful

26-50% of exits successful

51-75% of exits successful

76-99% of exits successful

100% successful exits

Total

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

34.780a

Likelihood Ratio