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Abstract

Many people hold the misconception that people with mental illness are dangerous and, consequently, should be feared. This stigma is reinforced by the overrepresentation of people with mental illness in the U.S. criminal legal system. This overrepresentation is due to ineffective policies making it difficult to adequately identify and treat this population within the legal system. To address this, two studies were conducted on CloudResearch. The first study examined public attitudes towards correctional policies and explored associated attitudinal factors. The second study built on these findings by testing interventions to decrease stigma and increase support for rehabilitation. Findings show that participants were more supportive of rehabilitative policies compared to punitive policies aimed at justice-involved people with mental illness. Participants’ fear of people with mental illness, fear of criminal behavior, perceived mutability of mental illness, and perceived mutability of criminal behavior were examined in relation to support for both punitive and rehabilitative policy. Imagined contact did not effectively increase support for rehabilitative policy, but education did. Future research can build on these findings by improving the interventions and identifying other attitudinal factors that could be paths for intervention.

Keywords

stigma policy support fear perceived mutability of criminal behavior perceived mutability of mental illness U.S.

Many people hold the belief that people with mental illness are dangerous (Ghiasi et al., 2022). This common misconception (Morabito & Socia, 2015) is reinforced by policy and rhetoric surrounding people with mental illness and their overrepresentation in the U.S. criminal legal system (Arafat et al., 2024; Bronson & Berzofsky, (2017). This overrepresentation began due to processes such as deinstitutionalization and is exacerbated by factors such as homelessness, unemployment (Gulati et al., 2019), and lack of resources. Additionally, lack of mental health treatment provided to justice-involved people (Arafat et al., 2024; James & Glaze, 2006), both while incarcerated and in the community, also leads to their overrepresentation within the system as people with mental illness are reprimanded more severely (Clark, 2018) often due to untreated symptomatology (Smith et al., 2019) which can lead to increased sentences. They are also more likely to encounter police (Desmarais et al., 2014). To ameliorate the overrepresentation of mental illness in the criminal legal system, we must improve the resources afforded to this group and create systems that prevent them from being incarcerated due to symptomatic behavior and more effectively promote desistance from criminal behavior. This can be accomplished by changing policy and the first step towards policy change is gaining public support (Corăbian et al., 2024). Thus, the aim of these studies is to increase public support for policy change, by first examining the public's current attitudes towards justice-involved people with mental illness, why people hold those attitudes, and testing if those attitudes can be changed.

Current Policy Responses for Justice-Involved People with Mental Illness

There are policies and structures targeting people with mental illness at multiple points in the U.S. criminal legal system where they can be intercepted and diverted. At the earliest point of contact with the criminal legal system—arrest—many communities have aimed to divert people with mental illness using a co-response model (Anestis et al., 2024; Morabito et al., 2018). Co-response models work to prevent the unnecessary admission of people into the criminal legal system by having a trained mental health professional and a police officer report to scenes where a mental health concern is likely involved (Krider et al., ((2020). By utilizing a trained mental health professional, co-response models provide a promising method to effectively deflect people with mental illness away from entering the legal system unnecessarily, as well as creating access to previously unavailable resources (Morabito et al., 2018; Yang & Lu, 2024).

Another diversion method implemented in the United States is mental health courts, which are a specialty treatment program within the court system that justice-involved people with mental health concerns can be diverted to (Bureau of Justice Assistance, 2012). These courts are composed of legal personnel (usually a judge, prosecutor, and defense attorney) who specialize in mental health and/or express interest in working with this specific population (Bureau of Justice Assistance, 2012). Although each court has its own eligibility criteria (California Judicial Branch, n.d.), the general aim is to redirect eligible participants away from the traditional legal system and connect them with community rehabilitative services and support networks (Texas Judicial Commission on Mental Health, n.d.). There are currently over 450 mental health courts active in the United States (SAMHSA, 2023) and, although the variability between mental health courts makes large-scale evaluations difficult, emerging research shows that they are effective at reducing recidivism (Jalain et al., 2024) and increasing life satisfaction (Nijdam-Jones & McNiel, 2020).

Some interventions aimed at people with mental illness in the legal system are controversial due to their use of coercion to engage people in mental health treatment. For example, California's CARE Act legalizes coerced treatment through the utilization of civil courts to enact court-ordered involuntary treatment and, if one continues not to comply, can result in involuntary commitment or legal conservatorship (Bazelon Center, 2022; Bossing, 2023). Although the CARE Act is specific to California, many states have some variation of forensic assertive community treatment (FACT), a practice that is intended to help justice-involved people with severe mental illness reintegrate back into their community through intensive outpatient supervision (Case Western Reserve University, 2021; Lamberti & Weisman, 2010; Office of Inspector General, 2018) Both the CARE Act and FACT appeal to the public's belief that people with mental illness are dangerous (Ghiasi et al., 2022) and should be required to obtain treatment against their will (Pescosolido et al., 2019). This may be why these policies generally receive more public attention, despite being harmful towards the people they are targeting.

Although research shows effectiveness of mandated programs (Coldwell & Bender, 2007) and coerced treatment is a widely accepted practice for treating mental illness, it is necessary to acknowledge the ethical concerns that scholars have raised. They argue that this type of treatment inherently entails human rights violations and is not in line with human rights-based mental healthcare (Sashidharan et al., 2019). These treatments violate the patient's rights to equality, liberty, and personal autonomy guaranteed by the United Nations Convention on the Rights of Persons with Disabilities (as cited in Sugiura et al., 2020). In addition to these violations, coercive care also introduces risk for both psychological and physical harms such as post-traumatic stress disorder, pulmonary embolism, cardiac arrest, and even death (see Aragonés-Calleja & Sánchez-Martínez, 2024 for a review). It is also argued that implementing coercive treatment only serves to further alienate people from seeking mental healthcare for fear of being held against their will or not being able to exit the treatment of their own volition (Swartz et al., 2003). Thus, by targeting public stigma surrounding justice-involved people with mental illness, it may be possible to decrease support for coercive practices and increase support for rehabilitative practices that aim to address both recidivism and symptomology.

Stigma About Mental Illness and Justice-Involved People

Justice-involved people with mental illness are highly stigmatized (Tremlin & Beazley, 2024). This is likely due to the belief that people with mental illness are dangerous (Ghiasi et al., 2022), which leads to increased avoidance or desire for social distance (Durna et al., 2019). This in turn leads to more negative attitudes about related policy issues (Barry & McGinty, 2014; Kennedy-Hendricks et al., 2017), which can result in greater exclusionary sentiments. Consequently, believing that people with mental illness are dangerous is positively associated with support for forced treatment (Pescosolido et al., 2019).

Although there is some evidence that people with schizophrenia or psychosis are more likely to be violent relative to average community members (Douglas et al., 2009; Fazel et al., 2009), especially during the first episode of psychosis (Nielssen & Large, 2010), these illnesses occur in a very small subset of people with mental illness. Additionally, even in this small subset of people with schizophrenia or psychosis, two meta-analyses found that the relationship between the illness and violent behavior was strongly impacted by the presence of a comorbid substance use disorder (Douglas et al., 2009; Fazel et al., 2009). When research has found a link between non-psychosis-related mental illness and violent behavior, it is also usually accompanied by a comorbid substance use disorder and, even then, ethnicity and gender were better predictors of violence than mental illness (Corrigan & Watson, 2005). Thus, when considering all people with mental illness, the idea that they are particularly dangerous is not reflective of reality, as having a mental illness does not inherently make a person more dangerous (Morabito & Socia, 2015) or more likely to commit crime (see Shipley & Borynski, 2013 for a review).

Changeable Factors Associated with Stigma

Limited knowledge of mental illness and lack of experience with people who have mental illness are factors associated with stigma that can be changed, making them ripe targets for anti-stigma interventions. People who have more knowledge about mental illness (Yin et al., 2020), have personal experience with someone who has a mental illness (Addison & Thorpe, 2004; Vaudreuil, 2022), or who view mental illness as more changeable (Falco & Turner, 2014) hold less stigmatizing attitudes towards people with mental illness and have greater support for rehabilitative policies (Falco & Turner, 2014). Perception of dangerousness is also strongly associated with stigma of mental illness (Ghiasi et al., 2022), so through increasing knowledge about and contact with people with mental illness, we may also be able to reduce the associated fear and reduce stigma surrounding this group.

We will also examine the effect that perceived mutability of both mental illness and justice involvement has on stigma. Perceived mutability refers to one's belief about the changeability of some factor. For our purposes, it refers to the belief about the changeability of mental illness and the changeability of criminal behavior. Research shows that people who view mental illness as more changeable hold less stigmatizing attitudes and have greater support for rehabilitative policies (Falco & Turner, 2014). Similarly, viewing criminal behavior as less changeable is associated with punitive attitudes (Grasmick & McGill, 1994; Shi et al., 2022; Sims, 2003; Updegrove et al., 2021) and higher levels of stigma (Shi et al., 2022). Consequently, it is expected that perceived mutability is the mechanism that drives the relationship between stigma and punitive attitudes, with more punitive attitudes predicting higher levels of stigma (Hernandez & Eno Louden, 2024).

Changing Attitudes to Increase Policy Support

Interventions that can target the factors associated with policy support and reduce stigma for justice-involved people include education, perceived variability (i.e., recognition that out groups are made up of diverse individuals), and contact. Research shows that both education about and contact with the targeted group can serve as interventions to decrease stigma (Thornicroft et al., 2016; Waqas et al., 2020). Education interventions involve providing people with information that is meant to elucidate things that are not common knowledge about the target group. This includes dispelling common misbeliefs and replacing them with correct information. Contact interventions involve having a positive social interaction with a member of the target outgroup. However, it can be difficult to create opportunities for people to have positive contact with members of a specific group, such as people who are justice-involved and have mental illness, due to limited access to members of the stigmatized groups and the resources required to facilitate such contact. As a result, studies have tested the effectiveness of imagined contact, where people are provided with a detailed description of a person from a target group and are instructed to imagine that they are having a positive interaction and getting to know each other (Stathi et al., 2012). This technique has been found to be effective in reducing stigma for groups including people with mental illness (Schuhl et al., 2019).

Present Studies

The present research focused on identifying the factors related to endorsed stigma about mental illness and justice involvement and tested whether possible interventions for decreasing endorsed stigma would result in increased support for more rehabilitative policies for these groups. Study 1 examined the current attitudes about contemporary rehabilitative and punitive policies, as well as assessed what other factors were associated with those attitudes.

H1: There is a difference in support for rehabilitative policies versus punitive policies aimed at justice-involved people with mental illness.

H1a: Punitive policies aimed at justice-involved people with mental illness are more supported than rehabilitative policies aimed at this group.

H2: As fear of people with mental illness and fear of crime increase, support for rehabilitative policies decreases and support for punitive policies increases.

H3: As perceived mutability of mental illness and criminal behavior increase, support for rehabilitative policies increases and support for punitive policies decreases.

These findings from Study 1 informed the creation of the interventions used in Study 2. Specifically, Study 2 tested whether education about or imagined contact with justice-involved people with mental illness would successfully change people's attitudes and increase support for rehabilitative policies targeted at this group.

H4: Both education and imagined contact will increase support for rehabilitative policies aimed at justice-involved people with mental illness.

H4a: Imagined contact will be more successful than education at increasing support for rehabilitative policies.

Study 1

Method

Participants. A power analysis performed on GPower for a multiple linear regression with an effect size of f² = .065, an α = .05 a power of .8, and nine tested predictors yielded a sample size of 250 participants. To account for attrition and unusable data, 10% was added to the sample size to achieve a final sample size of 275 participants.

Data were collected from 281 participants through CloudResearch, a subset of Amazon's MechanicalTurk, during October of 2023. To be eligible, participants had to be members of CloudResearch, living in the United States, over the age of 18, have an approval rating of at least 95%, and have completed at least 100 hits on CloudResearch. On CloudResearch, each project or survey that a person completes is counted as a “hit” and each person is given an overall approval rating based on the quality of data they have provided in the past (CloudResearch, n.d.). The eligibility criteria of having completed at least 100 hits and an approval rating of at least 95% ensured that those who were eligible to participate in the study have provided quality responses in the past and thus were more likely to provide quality data for the current study (Peer et al., 2014). Of the 281 participants, 11 were removed prior to analyses because they did not complete the necessary study procedures or they failed both attention checks in which they were asked to select a specific response to ensure they were reading the questions. This resulted in a final sample size of 270 participants. The average age of this sample was 37.86 years. See Table 1 for other demographic characteristics.

Table 1.
Study 1: Demographic Characteristics.

Characteristic n %

Gender

Female 178 65.9

Male 91 33.7

Prefer to self-describe 1 0.0037

Race

White 207 76.7

Black or African American 25 9.3

Native American or Alaskan Native 4 1.5

Asian 17 6.3

Other/Prefer not to answer 17 6.3

Latinx

Yes 33 12.2

No 236 87.4

Prefer not to answer 1 0.4

Highest level of education

High school diploma or GED 32 11.9

Some college, no degree 61 22.6

Associate's or technical degree 45 16.7

Bachelor's degree 85 31.5

Graduate or professional degree 46 17.0

Prefer not to answer 1 0.4

Political Ideology^a

Extremely liberal (0) 26 9.6

1 40 14.8

2 37 13.7

3 67 24.8

4 35 13.0

5 33 12.2

6 21 7.8

Extremely conservative (7) 11 4.1

^a
Mean = 3.05.

Materials and Measures. Study 1 had four attitudinal measures, an allocation task, and demographic questions. To obtain scores for each of the attitudinal measures, all items on a given measure were averaged, allowing for a maximum of one missing response per measure. Two attention checks asking participants to indicate a specific response were also integrated into the scales.

Fear of People with Mental Illness

Fear of people with mental illness was measured using the dangerousness/avoidance subscale of the Prejudice towards People with Mental Illness scale from Kenny et al. (2018). This subscale has a Cronbach's alpha of .87 and established validity (Kenny et al., 2018). It asks participants to indicate their agreement with statements about people who have a mental illness (e.g., “I would find it hard to talk to someone who has a mental illness”). This subscale consists of eight items that were answered on a 9-point Likert scale from Very Strongly Disagree* to Very Strongly Agree.

Fear of Crime

Fear of crime was measured using a scale by Grubb and Bouffard (2014). The scale has a Cronbach's alpha of .94 (Grubb & Bouffard, 2014) and is comprised of six items that were answered on a 4-point Likert scale from Not worried at all to Very Worried. A sample item from this measure is “How worried are you about being assaulted by someone?”

Perceived Mutability of Mental Illness

Perceived mutability of mental illness was assessed with six items answered on a 7-point Likert scale from Completely Disagree to Completely Agree. Five of these items were adapted from the treatability subscale of the Day's Mental Illness Stigma Scale (Day et al., 2007). The sixth item was created specifically for this project and was as follows: “People with mental illness can improve, whether it is through overcoming it, through getting it under control, or through some other means.”

Perceived Mutability of Criminal Behavior

Perceived mutability of criminal behavior was assessed with five items answered on a 6-point Likert scale from Strongly Disagree to Strongly Agree. Four of the items were adapted from the belief in redeemability questions (α = .72) used in Burton et al. (2020). These questions were adapted by Burton et al. (2020) for an adult population from Maruna and King (2009). The fifth item was created specifically for this project and was as follows: “If a person has committed crime in the past, it does not necessarily mean that they will commit crime in the future.”

Allocation Task

Support for rehabilitative and punitive public policy was operationalized using an allocation task modeled after Dunbar (2020). Participants were told that they had been given a budget to allocate across four public policies and were instructed to allocate whatever percentage they would like to each individual policy. The four policy options were drawn from the literature, where we chose two policies that were relatively well-known rehabilitative programs (increasing resources for co-response programs and increasing resources for mental health courts; Barry & McGinty, 2014), and two policies that are typical punitive responses (increasing police on the street to detect and apprehend criminals, and building more prisons/increasing prison sentences; Dunbar, 2020), each of these policies were accompanied by a short explanation of what they entail. The survey was constructed so that participants could not move on from the task unless all percentages equaled 100%. See Supplemental Material A for the full allocation task.

Demographics

Participants were also asked about their political orientation, age, highest level of education, gender, race, and ethnicity. Political orientation was measured on a sliding scale from 0 (Liberal) to 7 (Conservative).

Procedure. Both studies received approval from the University of Texas at El Paso's Institutional Review Board. After opting into the study, participants were redirected to the Qualtrics survey site to complete the study material. Before beginning the survey, potential participants were presented with a study information sheet that briefly described the study and provided resources should they feel any discomfort while taking the survey, although it was not expected that they would. This information sheet also emphasized that the study was voluntary and participants could quit at any time without penalty. After reading the information sheet, potential participants had to select “Agree to participate” as a form of virtual consent. Participants were then presented with all four measures in a counterbalanced order before completing the allocation task and answering demographic questions. After completing the survey, each participant was compensated $1. This amount was calculated using a pilot study of undergraduate participants to determine the average amount of time of the survey and following best practices recommended by CloudResearch (CloudResearch, n.d.), which suggest compensating participants $0.10 for every minute the study takes.

Analytic Strategy. Data cleaning and analyses for both studies were all conducted using version 27.0.1.0 of SPSS Statistics. Skewness and kurtosis for all measures were within ±1.00, which is considered an acceptable range for the assumption of normality (Kim, 2013). The responses from the allocation task for the two rehabilitative policy options (M = 72.67) were both statistically different from the two punitive policy options (M = 27.33), t(269) = 33.72, p < .001, d = 2.05. Thus, to calculate support for each type of policy, we summed the percentage allocated to both punitive policies into one variable and the percentage allocated to both rehabilitative policies into another variable. Because of the nature of the allocation task, the possible percent of budget allocated to either rehabilitative or punitive policies ranges from 0% to 100% and these values are dependent on each other as they must add to 100%. As a result, an equal amount of support to both types of policies would then be represented by each type of policy receiving 50% of the budget. Thus, to test our first hypothesis regarding a difference in support for rehabilitative policies versus punitive policies aimed at justice-involved people with mental illness, we conducted a single sample t-test comparing the mean support for rehabilitative policies to a value of 50, to represent the scenario of both types of policies being supported equally.

To test our second hypothesis, we conducted a multiple linear regression with support for rehabilitative policies as the continuous outcome variable. This continuous variable was calculated by subtracting the total percentage of funds allocated to punitive policies from the total percentage of funds allocated to rehabilitative policies. As a result, the support for rehabilitative policy variable can range from −100 to 100, where a negative score represents more support for punitive policies and a positive score represents more support for rehabilitative policies. This support for rehabilitative policy variable was then used as the dependent variable in a multiple regression with fear of mental illness, fear of crime, perceived mutability of mental illness, perceived mutability of criminal behavior, political ideology, and age as predictors. See Table 2 for correlations.

Table 2.
Study 1: Correlations.

Variable 1 2 3 4 5 6 7

1. Policy attitudes —

2. Fear of mental illness −.26 —

3. Fear of crime −.26 .13* —

4. Perceived mutability of mental illness .29 −.29 −.11 —

5. Perceived mutability of criminal behavior .34 −.39 −.19 .31 —

6. Age −.18 .22 −.19** −.05 −.13* —

7. Political Ideology −.50 .27 .10 −.16* −.34** .16* —

*p < .05; **p < .01.

Study 1 Results

Support for Rehabilitative and Punitive Policies

Participants’ support for policies was not equivalent, as support for rehabilitative policies (M = 72.67) was significantly different than 50%, t(269) = 16.81, p < .001, d = 1.03. Because support for punitive polices (M = 27.33) is dependent on support for rehabilitative policies, punitive policy support was also significantly different. Rehabilitative policies were more supported than punitive policies, as the mean support for rehabilitative policies was higher.

Relationship of Fear and Perceived Mutability with Support for Policy

The multiple linear regression model was significant, R² = .36, F(6, 250) = 22.91, p < .001. Fear of mental illness, b = −0.66, p = .733, and perceived mutability of criminal behavior, b = 5.56, p = .113, were not significant predictors of support for policy. However, fear of crime, b = −11.63, p < .001, sr² = .04, and perceived mutability of mental illness, b = 8.26, p = .003, sr² = .02, were. As fear of crime increased, people were more likely to be supportive of punitive policies. Conversely, as perceived mutability of mental illness increased, people were more likely to support rehabilitative policies. Additionally, participant age, b = −0.60, p = .015, sr² = .02, and political ideology, b = −9.14, p < .001, sr² = .13, were also significant predictors of support for policy, such that older age and more conservativeness were predictive of support for punitive policies. See Supplemental Material B for descriptive statistics.

Study 2

Method

Participants. A power analysis performed in GPower for an analysis of covariance (ANCOVA) with an effect size of f = .175, an a = .05, a power of .8, degrees of freedom numerator of two, three groups, and nine covariates yielded a sample size of 318 participants. To account for attrition and unusable data, 10% was added to the sample size to achieve a final sample size of 350 participants.

Participants were again recruited through CloudResearch with the same eligibility criteria used in Study 1 during October and November of 2023. Data were collected from a total of 359 participants. Of these, seven were removed for not completing the allocation task, 13 were removed for failing the manipulation check in which participants were asked a question about the content of the vignette they read, and one was removed due to failing both attention checks, yielding a final sample of 338 participants with a mean age of 37.31 years. See Table 3 for other demographic characteristics.

Table 3.
Study 2: Demographic Characteristics.

Characteristic n %

Gender

Female 235 69.5

Male 93 27.5

Prefer to self-describe 6 1.8

Prefer not to answer 4 1.2

Race

White 270 79.9

Black or African American 29 8.6

Native American or Alaskan Native 2 0.6

Asian 11 3.3

Native Hawaiian or other Pacific Islander 2 0.6

Multiracial 12 3.6

Other/prefer not to answer 12 3.6

Latinx

Yes 37 10.9

No 301 89.1

Highest level of education

Some high school or less 2 0.6

High school diploma or GED 38 11.2

Some college, no degree 86 25.4

Associate's or technical degree 58 17.2

Bachelor's degree 109 32.2

Graduate or professional degree 44 13.0

Prefer not to answer 1 0.3

Political ideology^a

Extremely liberal (0) 36 10.7

1 46 13.7

2 40 11.9

3 63 18.8

4 65 19.4

5 50 14.9

6 24 7.2

Extremely conservative (7) 11 3.3

^a
Mean = 3.12.

Materials and Measures. Study 2 included the same four measures, allocation task, demographic questions, and attention checks as Study 1. It also included an intervention administered through vignettes, as well as a manipulation check administered immediately after the intervention. As in Study 1, the scores for each of the attitudinal measures were obtained by averaging all the items on the respective measure, allowing for a maximum of one missing response per measure.

Participants in Study 2 were randomly assigned to one of three conditions: control, education, or imagined contact. Those in the control condition received a vignette about NASA. Those in the education condition received an educational vignette that included information about the treatability and mutability of mental illness. This vignette starts by sharing that it is common for mental illness to be viewed as incapacitating and untreatable but then goes on to provide research findings on how mental illness can be treated and its symptoms can be reduced. It also includes brief statistics showing that people with mental illness are not inherently dangerous and that mental illness does not typically cause criminal behavior.

Those in the imagined contact condition received a vignette that was modeled after the imagined contact vignette used in Schuhl et al. (2019) but was adapted to describe a justice-involved person with mental illness. This vignette begins by asking participants to imagine a scenario and then goes on to describe said scenario. The scenario begins with the participants on a busy train where they meet a man with whom they begin conversing. The conversation is described in a positive manner, includes shared interests between the participant and the man on the train, and is explicitly stated to have lasted for half an hour. See Supplemental Material C for full vignettes.

Procedure. Study 2 followed the same procedure as Study 1, but after they answered the attitudinal measures participants read their randomly assigned vignette. After reading their assigned intervention, participants answered a manipulation check before going on to complete the same allocation task and demographic questions that were completed in Study 1. Each participant was compensated $1.20 following best practices recommended by CloudResearch (n.d.) described in Study 1.

Analytic Strategy*. Skewness and kurtosis for all measures were within ±1.00, which is considered an acceptable range for the assumption of normality (Kim, 2013). As in the first study, the support for rehabilitative policy variable was calculated by subtracting the total percentage allocated to punitive policies from the total percentage allocated to rehabilitative policies. To answer our third research question, we conducted a one-way ANCOVA with intervention type as the independent categorical variable, support for rehabilitative policy as the dependent continuous variable, and significantly associated attitudinal variables as covariates (see Table 4 for correlations). We also planned to conduct multiple pairwise comparisons of the interventions using Fisher's Least Significant Difference test if the omnibus test was significant.

Table 4.
Study 2: Correlations.

Variable 1 2 3 4 5 6 7

1. Policy attitudes —

2. Fear of mental illness −.43 —

3. Fear of crime −.09 .09 —

4. Perceived mutability of mental illness .29 −.33 −.20 —

5. Perceived mutability of criminal behavior .37 −.52 −.15 .33 —

6. Age −.16 .21 −.17 .03 .01 —

7. Political ideology −.56 .39** −.03 −.20* −.27** .12* —

*p < .05; **p < .01.

Study 2 Results

Do the Interventions Increase Support for Rehabilitative Policies?

When controlling for participants’ fear of people with mental illness, perceived mutability of criminal behavior, and perceived mutability of mental illness, the interventions did have a small effect on participants’ support for policy, F(2, 331) = 3.87, p = .02, η_p² = .02. The pairwise comparison showed that support for rehabilitative policy was significantly higher in the education condition (M = 56.87), than in the control (M = 45.46) or the imagined contact (M = 43.60) condition. There was not a significant difference in support for rehabilitative policy between the control condition and the imagined contact condition. See Supplemental Material D for descriptive statistics.

General Discussion

Although there has been informative research conducted on those with mental illness (Corrigan et al., 2001) and those with criminal legal involvement (Pescosolido & Martin, 2015) individually, there has been less research conducted at the intersection of these identities. These results add to the growing body of literature on stigma reduction and interventions for increasing public support for rehabilitative policies aimed at justice-involved people with mental illness.

Punitive Versus Rehabilitative Policy

Contrary to our hypothesis, participants in both studies were more supportive of rehabilitative policies than punitive policies for justice-involved people with mental illness. It is possible that this is due to the demographic makeup of our samples. In both studies, our samples were mostly White, women, educated, and liberal. Research shows that these identities are associated with more rehabilitative attitudes (Applegate et al., 2002; Gonzales et al., 2017; Silton et al., 2011), so it is possible that the differences in support for policy were driven by the sample demographics.

However, it is more likely that the type of punitive policy options that were presented to participants elicited an unexpected response. Both of the punitive options (building more prisons/increasing prison sentences and increasing police on the street to detect) have been controversial in the recent past (Enns & Ramirez, 2018; Navarro & Hansen, 2023; Patil, 2018) and it is possible that many participants had a strong reaction against these options causing them to default their support to the rehabilitative policies. This possibility is reflected in the free responses in Study 1. Of the 216 participants who responded to the free response section, most were against helping prisons, often stating that they are not helpful for treating mental illness or deterring crime. These responses show that the punitive policies elicited polarized responses from participants, suggesting that participants were more supportive of rehabilitative policy for justice-involved people with mental illness. This is in line with other work (Baker et al., 2015; Vuk et al., 2020) that suggests that public sentiment has shifted towards more support for rehabilitative approaches.

Attitudes Associated with Support for Policy

Both studies found that fear of mental illness, perceived mutability of mental illness, and perceived mutability of criminal behavior were associated with support for policy. As in past research (Jorm et al., 2012), higher levels of fear of people with mental illness were associated with more support for punitive policies and less support for rehabilitative policies. Also in line with past research (Falco & Turner, 2014; Updegrove et al., 2021), higher levels of perceived mutability of both mental illness and criminal behavior were associated with less support for punitive policies and more support for rehabilitative policies. Fear of crime was only associated with support for policy in Study 1, with higher levels being associated with more support for punitive polices and less support for rehabilitative policies.

In partial support of our hypotheses, Study 1 found that participants’ fear of mental illness and perceived mutability of criminal behavior were not predictors of their support for policy, but their fear of crime and their perceived mutability of mental illness were. These relationships occurred such that participants who had higher fear of crime were more supportive of punitive policies and those who had lower fear of crime were more supportive of rehabilitative policies. In contrast, those who reported more perceived mutability of mental illness tended to be more supportive of rehabilitative polices and those who reported less perceived mutability of mental illness tended to be more supportive of punitive policies.

Study 2 found that, of the four attitudinal variables of interest, participants’ fear of mental illness and their perceived mutability of both criminal behavior and mental illness were predictive of their support for policy. This is in contrast to Study 1 that showed perceived mutability of criminal behavior was not a predictor and fear of crime was. The finding that perceived mutability of criminal behavior was a significant predictor in Study 2, but not in Study 1 is likely due to the differences in the models being tested. In Study 1, all four attitudinal variables were correlated with support for policy, so they were all included in the model. This regression showed that fear of crime and mutability of mental illness were driving the predictive relationship. However, in Study 2, fear of crime was not significantly correlated with policy support so it was not included in the model. This likely allowed the mutability of crime variable to have more explanatory power for policy support, since the overlap with fear of crime was no longer introduced.

Potential Interventions for Stigma of Justice-Involved People with Mental Illness

Contrary to other studies (Schuhl et al., 2019), Study 2 found that imagined contact was no better than an unrelated reading at increasing support for rehabilitative policy. Additionally, when controlling for participants’ fear of people with mental illness, perceived mutability of mental illness, and perceived mutability of criminal behavior, support for policy did differ significantly across intervention types. Specifically, as in previous research (Corrigan et al., 2001), the education intervention significantly increased support for rehabilitative policies.

One possible explanation as to why education was effective (when imagined contact was not) is the difference in stigma related content in both interventions. The imagined contact intervention was meant to reduce stigma through humanizing someone from the stigmatized group. Thus, justice involvement and mental illness were each only mentioned once within the intervention to state the person held both identities. In contrast, the education intervention was meant to reduce stigma through disputing the idea that mental illness makes people more dangerous and providing information about the treatability of mental illness. As a result, mental illness is focused on throughout the entire intervention whereas justice involvement is only mentioned in half of it. Although we are not aware of any research that has tested this, it is possible that the public stigma of justice involvement is more severe (and, consequently, harder to change) than the public stigma of mental illness causing the education intervention's emphasis on mental illness-related information to result in a decrease in that stigma. In other words, if the public stigma of mental illness is more easily changed and the education intervention focused more on mental illness, then it is possible that it triggered the stigma of mental illness that could be more readily influenced, whereas the imagined contact intervention only mentioned each identity once, so it did not trigger these differing amounts of stigma.

Another possibility is that the imagined contact intervention was simply too short to have any notable impact on participants. Traditional contact involves in-person conversations with a person from the outgroup, but imagined contact is relying on participants’ cognitive resources to imagine such an interaction. Consequently, it's possible that—for it to be effective—imagined contact requires more thorough and detailed descriptions of the interaction to make it more similar to traditional contact than the length of these interventions allowed for. Perhaps including a script or narrative retelling of the participants “conversation” with the person from the outgroup would allow the participants to properly engage with the scenario, both in terms of length and substance. In addition to this, imagined contact scenario likely requires more cognitive effort from participants than the education vignette. In the latter they are simply asked to read and presumably absorbed the information provided, but in the former they are asked to imagine something that is being described by the vignette. This is a more difficult task compared to reading the educational vignette and it is very likely that participants vary in their ability to imagine said scenario and in the extent to which they chose to engage with it.

Limitations

These studies were limited by convenience sampling through CloudResearch, resulting in samples that were predominantly White, educated, female, and liberal. While the platform provides high-quality data (Douglas et al., 2023) and best practices were followed, as described in the methods, the sample's demographic composition restricts broad generalizability. The use of convenience sampling rather than random sampling likely produced biased standard errors. Nevertheless, the findings contribute meaningful insights to the existing literature. Additionally, although we did find an effect of the education intervention, both interventions used were relatively short, so it is possible that participants were not able to internalize the information given such brief readings and longer interventions may produce more robust findings. However, the purpose of these studies was specifically to test the utility of short online interventions to examine if they could bridge the gap in contact with groups where in-person contact is not easily facilitated. Lastly, it is important to note that the relationship between support for rehabilitative and punitive policy is much more nuanced (see Vuk et al., 2020 for a review) than the allocation task allows researchers to account for. It is likely that people endorse both types of policies simultaneously (Vuk et al., 2020) and these findings are looking at a very specific portion of their overall endorsements for punitive and rehabilitative policy.

Implications

To our knowledge, these studies are the first to attempt to understand attitudes towards and test interventions to decrease stigma for justice-involved people with mental illness. Although we did not find support for the imagined contact intervention and the effect sizes for our significant findings are relatively small, our findings still provide the first step to understanding the current support for rehabilitative and punitive policies aimed at justice-involved people with mental illness, as well as identifying some of the individual factors associated with that support. From these findings, we can continue to improve existing interventions and develop other interventions for reducing stigma towards this group with the goal of increasing support for rehabilitative policies.

Future research can build on our findings by utilizing more demographically representative samples to explore different interventions to increase support for rehabilitative policies. Slightly longer educational interventions could be tested, though it is important that the length is not arduous for participants. This would allow the vignettes to convey more information and emphasize specific parts. Another possibility is to approach the contact intervention differently. Research shows that positive in-person interactions with someone from the target group decreases stigma towards that group (Corrigan et al., 2001). Unfortunately, this is hard to facilitate for many groups of interest, so imagined contact was created as an alternative. However, it may be too abstract for some people resulting in it being ineffective. A potential compromise between these two interventions could be digital contact with a person from the group of interest. Some research has been conducted in other areas showing that vicarious contact—watching a video of an interaction between an ingroup member and an outgroup member—is effective at reducing negative attitudes towards the outgroup (Vezzali et al., 2014). Thus, we could test a sort of simulated interaction using video where participants could have an “interaction” in which they ask questions getting to know the person in the video. This may look like having a large pool of prerecorded responses that a researcher has to play in response to the participant asking a question or it may be possible to set up a virtual “conversation” in which the participant is given a wide range of options they can ask the person and selecting that question automatically plays the prerecorded video response. When interventions that effectively reduce stigma and increase support for rehabilitative policies aimed at justice-involved people with mental illness are identified, research can then turn to testing their effectiveness across time and examine their effectiveness in other stigmatized groups.

Supplemental Material

sj-docx-1-fmh-10.1177_14999013251372626 - Supplemental material for Potential Interventions for Policy Support Targeting Justice-involved People with Mental Illness

Supplemental material, sj-docx-1-fmh-10.1177_14999013251372626 for Potential Interventions for Policy Support Targeting Justice-involved People with Mental Illness by Betel Hernandez and Jennifer Eno Louden in International Journal of Forensic Mental Health

Characteristic	n	%
Gender
Female	178	65.9
Male	91	33.7
Prefer to self-describe	1	0.0037
Race
White	207	76.7
Black or African American	25	9.3
Native American or Alaskan Native	4	1.5
Asian	17	6.3
Other/Prefer not to answer	17	6.3
Latinx
Yes	33	12.2
No	236	87.4
Prefer not to answer	1	0.4
Highest level of education
High school diploma or GED	32	11.9
Some college, no degree	61	22.6
Associate's or technical degree	45	16.7
Bachelor's degree	85	31.5
Graduate or professional degree	46	17.0
Prefer not to answer	1	0.4
Political Ideology^a
Extremely liberal (0)	26	9.6
1	40	14.8
2	37	13.7
3	67	24.8
4	35	13.0
5	33	12.2
6	21	7.8
Extremely conservative (7)	11	4.1

Variable	1	2	3	4	5	6	7
1. Policy attitudes	—
2. Fear of mental illness	−.26**	—
3. Fear of crime	−.26**	.13*	—
4. Perceived mutability of mental illness	.29**	−.29**	−.11	—
5. Perceived mutability of criminal behavior	.34**	−.39**	−.19**	.31**	—
6. Age	−.18**	.22**	−.19**	−.05	−.13*	—
7. Political Ideology	−.50**	.27**	.10	−.16*	−.34**	.16*	—

Characteristic	n	%
Gender
Female	235	69.5
Male	93	27.5
Prefer to self-describe	6	1.8
Prefer not to answer	4	1.2
Race
White	270	79.9
Black or African American	29	8.6
Native American or Alaskan Native	2	0.6
Asian	11	3.3
Native Hawaiian or other Pacific Islander	2	0.6
Multiracial	12	3.6
Other/prefer not to answer	12	3.6
Latinx
Yes	37	10.9
No	301	89.1
Highest level of education
Some high school or less	2	0.6
High school diploma or GED	38	11.2
Some college, no degree	86	25.4
Associate's or technical degree	58	17.2
Bachelor's degree	109	32.2
Graduate or professional degree	44	13.0
Prefer not to answer	1	0.3
Political ideology^a
Extremely liberal (0)	36	10.7
1	46	13.7
2	40	11.9
3	63	18.8
4	65	19.4
5	50	14.9
6	24	7.2
Extremely conservative (7)	11	3.3

Variable	1	2	3	4	5	6	7
1. Policy attitudes	—
2. Fear of mental illness	−.43**	—
3. Fear of crime	−.09	.09	—
4. Perceived mutability of mental illness	.29**	−.33**	−.20**	—
5. Perceived mutability of criminal behavior	.37**	−.52**	−.15**	.33**	—
6. Age	−.16**	.21**	−.17**	.03	.01	—
7. Political ideology	−.56**	.39**	−.03	−.20*	−.27**	.12*	—

Footnotes

ORCID iDs

Betel Hernandez

Jennifer Eno Louden

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

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

Supplemental Material

Supplemental material for this article is available online.

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