Understanding Persons With Mental Illness Who Are and ... - PsycNET

Law and Human Behavior 2013, Vol. 37, No. 3, 175–186

© 2012 American Psychological Association 0147-7307/13/$12.00 DOI: 10.1037/lhb0000013

Understanding Persons With Mental Illness Who Are and Are Not Criminal Justice Involved: A Comparison of Criminal Thinking and Psychiatric Symptoms Nicole R. Gross and Robert D. Morgan

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Texas Tech University Research has begun to elucidate that persons with mental illness become involved in the criminal justice system as a result of criminality and not merely because of their mental illness. This study aims to clarify the similarities and differences in criminal thinking and psychiatric symptomatology between persons with mental illness who are and are not criminal justice involved. Male and female (n ⫽ 94) participants admitted to an acute psychiatric facility completed measures to assess criminal thinking (i.e., Psychological Inventory of Criminal Thinking Styles and Criminal Sentiments Scale–Modified) and psychiatric symptomatology (Millon Clinical Multiaxial Inventory–Third Edition). In addition to the inpatient sample, 94 incarcerated persons with mental illness from a previously conducted study were selected based on their match with the current sample on several key demographic and psychiatric variables. The results of this study indicated that hospitalized persons with mental illness with a history of criminal justice involvement evidenced similar thinking styles to persons with mental illness who were incarcerated. Persons with mental illness without criminal justice involvement evidenced fewer thinking styles supportive of a criminal lifestyle than the incarcerated sample. Furthermore, the persons with mental illness sample with no history of criminal justice involvement showed significantly lower levels of psychopathology shown to be risk factors for criminal justice involvement (e.g., antisocial personality, drug dependence, alcohol dependence). These findings have implications for offender-type classification, development of targeted treatment interventions, and program placement. Keywords: criminal thinking, offender, criminal justice involvement, mental illness

When compared to offenders without mental illness, PMI who are placed in community supervision (i.e., probation and parole) after being released from a correctional facility are significantly more likely to recidivate (continued criminal behavior resulting in arrest and reincarceration; Messina, Burdon, Hagopian, & Prendergast, 2006). Likewise, it is estimated that 37–53% of PMI released from mental health facilities psychiatrically recidivate (decompensate and are consequently readmitted to a mental health facility) within 1 year of being discharged (Hillman, 2001; Segal & Burgess, 2006). Commonalities such as high criminal recidivism and psychiatric hospitalization rates between PMI who are and are not CJ involved may indicate common risk factors such as criminal thinking, poverty, homelessness, and unemployment (Draine, Salzer, Culhane, & Hadley, 2002; Mgustshini, 2010) between the two groups. Such results would identify a neglected treatment area for PMI regardless of setting (CJ or mental health) that, if addressed, may improve treatment outcomes (e.g., symptom reduction, reduced criminal recidivism, and psychiatric hospitalizations). Investigating the role of mental illness in the provocation and exacerbation of criminal behavior is thus warranted. PMI who are CJ involved may have unique mental health needs and criminal risk factors when it comes to offending behavior. Additionally, similarities in criminal thought patterns may affect psychological functioning and mental health recovery (e.g., symptom management, rehospitalization) of PMI who are not CJ involved. Although it may seem plausible that PMI enter the CJ system as a result of their mental health symptoms, it has been suggested that some PMI have comorbid criminal dispositions that result in their

Persons with mental illness (PMI) are 3 times more likely to be incarcerated than admitted to a psychiatric facility (Abramsky & Fellner, 2003; Torrey, Kennard, Eslinger, Lamb, & Pavle, 2010). Consequently, correctional institutions have become the largest providers of mental health treatment in the United States (Abramsky & Fellner, 2003). Notably, 14.5% of male and 31% of female offenders in jails have a serious mental illness (i.e., schizophrenia spectrum disorder; schizoaffective disorder; schizophreniform disorder; brief psychotic disorder; delusional disorder; psychotic disorder not otherwise specified [NOS]; bipolar disorder I, II, and NOS; major depressive disorder; and depressive disorder NOS; Steadman, Osher, Robbins, Case, & Samuels, 2009). PMI are disproportionally represented in correctional institutions because less than 6% of the general population is estimated to suffer from a severe mental illness (American Psychiatric Association, 2000; Kessler, Chiu, Demler, & Walters, 2005). It appears that PMI are involved in and affected across criminal justice (CJ) and mental healthcare systems; however, it remains unclear how PMI involved in the mental healthcare system compare to PMI involved in the CJ system.

This article was published Online First October 29, 2012. Nicole R. Gross and Robert D. Morgan, Department of Psychology, Texas Tech University. Correspondence concerning this article should be addressed to Robert D. Morgan, Department of Psychology, Texas Tech University, Box 42051, Lubbock, TX 79409-2051. E-mail: [email protected] 175


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CJ involvement (Hiday, 1999). Elbogen and Johnson (2009) found that severe mental illness did not predict future violence if not paired with historical, clinical, and dispositional contextual factors (e.g., past violence, unemployment, low socioeconomic status, victimization; Draine et al., 2002). Although violence does not necessarily result in CJ involvement, the aforementioned contextual factors that are related to the prediction of violence are also commonly associated with crime in general. Furthermore, Draine and colleagues (2002) noted that the relationship between mental illness and crime is weak, and they suggest that poverty, lack of education, unemployment, and limited prosocial relationships likely serve as moderating variables in this relationship. Mental illness appears to predispose individuals to reside in environments that foster criminal behavior (Draine et al., 2002; Fisher, Silver, & Wolff, 2006). For example, offenders and PMI often live in lowincome areas, are single, have limited social and family support, and have a history of unemployment (Fisher et al., 2006). Homelessness and a family history of incarceration have been found to be more prevalent among PMI who are CJ involved than offenders without mental illness (Ditton, 1999). Additionally, PMI who are CJ involved were more likely to be unemployed before their arrest when compared with offenders without mental illness (Ditton, 1999), and male PMI with substance abuse disorders were twice as likely to have a criminal record as those without a substance abuse disorder. This is likely due to the role of substance abuse as a prominent risk factor for criminal behavior (Andrews & Bonta, 2006). Among a sample of hospitalized veterans, substance abuse accounted for most of the variance in the risk of incarceration despite the presence or absence of mental illness (Erickson, Rosenheck, Trestman, Ford, & Desai, 2008). Additionally, the link between mental illness and violent behavior was weak if the individual did not have comorbid substance use issues (Elbogen & Johnson, 2009; Swartz et al., 1998). PMI appear to experience multiple risk factors that strongly influence CJ involvement. With regards to the number and intensity of risk factors experienced by PMI, Girard and Wormith (2004) found that PMI evidenced higher scores on the Levels of Service Inventory/Case Management Inventory (LSI/CMI; a commonly used measure of risk assessment for predicting criminal recidivism) than persons without mental illness, suggesting that PMI experience more criminal risk factors than individuals without mental illness. Many of these criminal risk factors measured by the LSI (e.g., education, employment, housing, substance abuse) correspond to poverty, joblessness, and other factors that Draine and colleagues (2002) identified as factors that predispose PMI to engage in criminal behavior. The criminal risk factors measured by the LSI/CMI are positively correlated with criminal recidivism. In a study examining PMI perceived risk for psychiatric rehospitalization, individuals endorsed risk factors such as unemployment, lack of education, lack of housing, and economic difficulties (Mgustshini, 2010) that correspond to those used in the prediction of criminal behavior. Thus, it is reasonable to suggest that criminal risk factors may also play a role in the frequency of psychiatric hospitalizations. These predisposing environmental factors are important in terms of treatment and recidivism (criminal and psychiatric) given that they increase the potential for the presence of criminal attitudes and thought patterns in PMI, even if they are not currently engaging in criminal behavior.

Carr and colleagues (2009) examined criminal thinking styles in a sample of civil psychiatric patients and compared the results to findings from a previously published study using offenders without mental illness. Results indicated that the civil psychiatric patients scored significantly higher on five of eight criminal thinking style scales. Morgan and colleagues (2010) examined criminal thinking and psychiatric symptomatology in a sample of 416 incarcerated PMI. It was found that incarcerated PMI exhibited criminal thinking patterns similar to those of incarcerated offenders without mental illness. Additionally, the psychiatric symptomatology of the incarcerated PMI was similar to that of inpatient psychiatric samples. These comparisons were made with prevalence rates known from other published samples of offenders without mental illness (criminal thinking comparison) and nonoffender psychiatric patients (psychiatric symptomatology comparison). Furthermore, a recent study conducted with 4,204 incarcerated male and female participants found results consistent with Carr and colleagues (2009) and Morgan et al. (2010) such that incarcerated PMI evidenced similar criminal thinking styles when compared with those without mental illness (Wolff, Morgan, Shi, Fisher, & Huening, 2011). Additionally, those with severe mental illness (i.e., schizophrenia or bipolar disorder) displayed higher levels of criminal thinking than those without mental illness and those with less severe mental illness (i.e., depression, posttraumatic stress disorder, and anxiety; Wolff et al., 2011). Although these three studies provided valuable information for understanding PMI that are and are not CJ involved, significant research design and methodological problems limited conclusive interpretations. Specifically, Carr and colleagues (2009) and Morgan and colleagues (2010) lacked a direct comparison group, and Wolff et al. (2011) was limited in that there was no mental health sample that was not CJ involved as a control. The proposed study aims to extend these prior studies by examining criminal thinking with PMI who are and are not CJ involved and provide a direct comparison group for Morgan et al. (2010). Results from Morgan et al. (2010) suggested that PMI who are CJ involved may have different psychiatric needs and features when compared with PMI who are not CJ involved. Furthermore, general criminal thinking has been shown to partially mediate the relationship between mental illness and institutional violence for incarcerated PMI (Walters, 2011), suggesting that the behavior of PMI who act violently should be considered a product of more than mental illness. It appears that PMI who are CJ involved are not merely criminals because of their mental illness but are “criminals who happen to be mentally ill” (Morgan et al., 2010). This assertion has important implications for PMI who are CJ involved, but the applicability of these results to PMI who are not CJ involved has yet to be empirically examined. Thus, the exploration of criminal thinking in PMI is warranted. If criminal thought patterns are found within PMI, or a subset of PMI, who are not CJ involved, what effects do such dispositions have on other aspects of functioning (e.g., mental health functioning)? To expand on Morgan et al. (2010), criminal thinking and psychiatric symptomatology data from PMI admitted to a short-term psychiatric facility were gathered to examine potential differences between this group and incarcerated PMI. Because mental illness and criminal propensities are conceptualized as comorbid disorders (Morgan et al., 2010; Wolff et al., 2011), the presentation and effects of such comorbidity may manifest differently in PMI who are not CJ


UNDERSTANDING PERSONS WITH MENTAL ILLNESS

involved. Additionally, the data collected from PMI who are not CJ involved will allow for the examination of criminal thinking as a risk factor in predicting the number of psychiatric hospitalizations and relapse. The purpose of this study was to further examine differences and similarities in psychiatric symptoms and criminal thinking between PMI who are incarcerated and psychiatric inpatients. The aim of the proposed study was to identify distinguishing features of PMI who are and are not CJ involved. It was hypothesized that all three groups would evidence similar overall levels of psychiatric symptomatology and produce similar symptom profiles. Such results would coincide with the results from Morgan et al. (2010). It was hypothesized that PMI admitted to the short-term psychiatric hospital without a history of CJ involvement would evidence less criminal thinking, as measured by the Psychological Inventory of Criminal Thinking Styles (PICTS) and the Criminal Sentiments Scale–Modified (CSS-M), than the PMI who were incarcerated or admitted to the short-term psychiatric facility with a history of CJ involvement. Lastly, it was expected that criminal thinking would be positively correlated with psychiatric hospitalizations such that those evidencing higher levels of criminal thinking would also have a greater number of lifetime psychiatric hospitalizations.

Methods The following method section is a replication of that found in Morgan et al. (2010). The procedure and all measures used, with the exception of a modified demographic form, are the same as those used in Morgan et al. (2010). This replication was necessary to allow for a direct comparison between the PMI sample admitted to a short-term psychiatric hospital in this study with the incarcerated PMI sample in Morgan et al. (2010).

Participants Participants consisted of 94 short-term psychiatric male (n ⫽ 53, 56.4%) and female (n ⫽ 41, 43.6%) patients from an acute psychiatric hospital located in West Texas. Participants were at least 18 years old (M ⫽ 38.55, SD ⫽ 11.35) and were admitted to the facility for at least 5 days (M ⫽ 8.24, SD ⫽ 7.02). We incorporated a 5-day minimum for the length of time admitted to the facility to increase the likelihood that the participant would be experiencing chronic and enduring psychiatric problems and not transient, crisis-type issues. Over half of the sample (n ⫽ 51, 54.3%) had been convicted of a crime in the past; for that reason, the inpatient sample was split into two groups (i.e., with and without past CJ involvement) for the completion of data analysis. Approximately 80% (n ⫽ 76) of the inpatients had been admitted to a psychiatric facility before their admission at the time of participation. Additionally, the Structured Clinical Interview for DSM–IV Axis I Disorders (SCID-I) was administered by a doctoral-level counseling psychology student (who received supervision and training from a licensed psychologist) to approximately 20% of the sample to assess the reliability of the participant’s diagnosis as reported by the institutional file. It appears that the institutional file provided a reliable diagnosis for the participants because there was a 77.8% agreement rate between the diagnosis found in the institutional file and the diagnosis determined by the administration of

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the SCID-I. On the basis of the diagnoses recorded for each participant from institutional records, the inpatient sample had a primary Axis I diagnosis of bipolar disorders (n ⫽ 37, 39.4%) followed by major depressive disorder (n ⫽ 24, 25.5%), schizophrenia (n ⫽ 11, 11.7%), schizoaffective disorder (n ⫽ 9, 9.6%), other mood disorders (e.g., drug-induced, NOS; n ⫽ 8, 8.5%), adjustment disorder (n ⫽ 2, 2.1%), psychosis NOS (n ⫽ 1. 1.1%), acute stress disorder (n ⫽ 1. 1.1%), and Asperger’s syndrome (n ⫽ 1. 1.1%). Participants were also drawn from those who participated in the Morgan et al. (2010) study. This sample was composed of 94 incarcerated male (n ⫽ 53, 56.4%) and female (n ⫽ 41, 43.6%) adults with mental illness. Participants were selected based on their match with participants in our sample on sex, Axis I diagnosis, age, ethnicity, years of formal education, and relationship status when possible. Analyses revealed no significant differences among significant demographic characteristics of Axis I diagnosis, age, race, and relationship status. However, the two groups differed significantly with regards to the number of years of formal education completed, t ⫽ 2.81, p ⫽ .005, with the inpatient mental health sample having completed more years of formal education (M ⫽ 12.46, SD ⫽ 2.13) than the incarcerated mental health sample (M ⫽ 11.52, SD ⫽ 2.44). Demographic characteristics of both groups along with the results of the between-group analyses of the demographics of each sample are shown in Table 1.

Materials A written informed consent form was used to inform potential participants of the purpose of the study, the potential risks, confidentiality, and their rights as human subjects. A self-report demographic form was used to gather information regarding age, ethnicity, relationship status, time hospitalized, CJ involvement, psychiatric history (e.g., treatment, number of hospitalizations), mental health diagnoses, employment status, and public assistance received. The PICTS (Walters, 1995), a self-report measure composed of 80 items and designed to assess thought patterns associated with criminal behavior (Walters, 2006), was used. For example, the PICTS contains items such as “The more I got away with crime the more I thought there was no way the police or authorities would ever catch up with me”; “The way I look at it, I’ve paid my dues and am therefore justified in taking what I want”; and “I have justified selling drugs, burglarizing homes, or robbing banks by telling myself that if I didn’t do it someone else would.” Responses to the items on the PICTS are provided using a 4-point Likert scale (1 ⫽ disagree, 4 ⫽ strongly agree; Walters, 2006). The PICTS produces two content scales (i.e., Current Criminal Thinking and Historical Criminal Thinking), two composite scales (i.e., Proactive Criminal Thinking and Reactive Criminal Thinking), eight thinking style scales (i.e., Mollification, Cutoff, Entitlement, Power Orientation, Sentimentality, Superoptimism, Cognitive Indolence, and Discontinuity), and five Factor and Special Scales (i.e., Problem Avoidance, Interpersonal Hostility, Self-Assertion, Denial of Harm, and Fear of Change; Walters, 2006). There are no cutoff scores distinguishing the presence or absence of each of the eight criminal thinking scales, but guidelines are provided for interpreting the criminal thinking style T-scores as low (⬍40),

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178 Table 1 Between-Sample Comparison of Demographic Results

Psychiatric inpatient sample


n Ethnicity Caucasian Hispanic African American Asian American Indian Other Diagnosis Schizophrenia/Other Psychotic Disorder Bipolar I and II MDD/Other Mood Disorder Other Mental Health Disorder Relationship Status Single Partnered/Common Law Divorced Separated Married Widowed

Age Education (Years)

Morgan et al., (2010) sample

%

n

%

54 20 8 1 1 9

58.1 21.5 8.6 1.1 1.1 9.7

51 13 22 0 0 8

54.3 13.9 23.4 0.0 0.0 8.5

21 38 29 6

22.3 40.4 30.9 6.4

20 38 32 4

21.3 40.4 34.0 4.3

34 5 21 8 7 5

42.5 6.3 26.3 10.0 8.8 6.3

43 4 23 9 15 0

45.7 4.3 24.5 9.6 15.9 0.0

␹2

p

10.16

0.07

0.57

0.90

8.15

0.15

M

SD

M

SD

t

p

38.55 12.46

11.35 2.13

36.83 11.52

10.61 2.443

1.08 2.81

0.28 0.005

average (ⱖ40, ⬍60), high (ⱖ60, ⬍70), and very high (ⱖ70; Walters, 2006). The PICTS has acceptable validity when compared with other means of assessing criminality (i.e., criminal history such as arrests, diversity of offenses, age of first offense, and psychopathy) (Walters, 2006; Walters & Schlauch, 2008). The PICTS has demonstrated moderate to high levels of internal consistency and test-retest reliability in offender samples. The internal consistency for the subscales ranged from .54 to .88 for male and female offenders (Walters, 1995; Walters, Elliott, & Miscoll, 1998). Test-retest reliability was .68 –.85 after 2 weeks and .57–.72 after 12 weeks (Walters, 1995; Walters et al., 1998). The PICTS has not been normed on a clinical sample. The internal consistency for the PICTS in this study was high, yielding a Cronbach’s ␣ of .95. The CSS-M (Simourd, 1997), a self-report measure composed of 41 items, designed to assess “attitudes, values, and beliefs related to criminal behavior” (Wormith & Andrews, 1984), was used. The CSS-M measures the content of criminal thoughts whereas the PICTS measures the process of criminal thinking (Simourd & Olver, 2002). For example, the CSS-M contains items such as “The police are as crooked as the people they arrest,” “Pretty well all laws deserve our respect,” and “You cannot get justice in court.” Responses to the items on the CSS-M are provided using a 3-point Likert-type scale (Simourd, 1997; Simourd & Olver, 2002). The CSS-M yields a total score and five subscale scores (attitude toward the law [Law], attitude toward the court [Court], attitude toward the police [Police], tolerance for law violations [TLV], and identification with criminal others [ICO]; Simourd, 1997; Simourd & Olver, 2002; Simourd & van de Ven, 1999). The Law, Court, and Police subscales are then combined to form the Law-Court-Police (LCP) subscale that assesses the level of respect an individual has for the criminal and legal system

(Simourd & Olver, 2002). Additionally, the TLV subscale assesses the degree to which an individual justifies their criminal behavior, and the ICO subscale assesses how the individual perceives the criminal behavior of others (Simourd & Oliver, 2002). Scores greater than 19 indicate clinical significance whereas scores of 30 or higher are considered “high” (Simourd, 1997). The CSS-M has not been normed on a clinical sample; however, it has been shown to be a valid and reliable measure with offender populations (Andrews, Wormith, & Kiessling, 1985; Roy & Wormith, 1985; Wormith & Andrews, 1984). Internal consistency ranged from .73 to .91 (Simourd, 1997; Simourd & Olver, 2002). Additionally, when compared with other criminal risk assessment measures (i.e., Hare Psychopathy Checklist–Revised and Level of Service Inventory–Revised), the convergent validity ranged from .25 to .37 (Simourd, 1997). The internal consistency for the CSS-M in this study was high, yielding a Cronbach’s ␣ of .88. The Millon Clinical Multiaxial Inventory–Third Edition (MCMI-III; Millon, 1994) was used. MCMI-III is a self-report measure composed of 175 true/false items, providing an integrated understanding of a respondent’s personality and clinical syndromes (Millon, 1994). The MCMI-III yields 14 Personality Disorder Scales that coincide with Diagnostic and Statistical Manual– 4th Edition (DSM–IV; American Psychiatric Association, 1994) Axis II disorders, and there are 10 Clinical Syndrome Scales that coincide with DSM–IV Axis I disorders (Millon, 1994). A Correction Scale detects careless or random responding, and the Modifying Indices and the Validity Index assess validity and response style (Millon, 1994). MCMI-III is strongly correlated with the MCMI-II, with correlations ranging from .59 to .88 (Millon, Davis, & Millon, 1997). The MCMI-III has been designed for use with clinical populations and has been normed on a clinical population.



Validity of the MCMI-III with relation to the diagnostic determination from clinicians was low to moderate, and the correlations ranged from .07 to .37 (Millon, 1994). Internal consistency reliability ranged from .66 to .90 whereas test–retest reliability ranged from .82 to .86 at 5–14 days from the original test date (Millon, 1994). The internal consistency for the MCMI-II in this study was high, yielding a Cronbach’s ␣ of .92. SCID-I (First, Spitzer, Gibbon, & Williams, 1997), a semistructured interview used to assist in diagnosing DSM–IV Axis I disorders that is composed of six modules that assess mood episodes, mood disorders, psychotic symptoms, psychotic disorders, substance abuse disorders, and anxiety and other disorders (First et al., 1997), was used in this study. During administration, various questions regarding mental health symptoms are posed to the examinee, and on the basis of their response the examiner determines the presence or absence of the symptom (First et al., 1997). The positive and negative ratings of symptoms within a diagnostic category are combined to determine if DSM–IV diagnostic criteria for the disorder have been satisfied (First et al., 1997). In terms of Kappa ratings, interrater reliability for the SCID-I ranges from .57 to 1.0, and test–retest reliability over a 7- to 10-day period ranges from .35 to .78 (Zanarini, et al., 2000). SCID-I has been developed as a means of improving the diagnostic accuracy of clinicians; thus, most comparisons with unstructured interviews or the best estimate diagnosis procedure have demonstrated superior validity for the SCID-I (Basco et al., 2000).

Procedure Individuals admitted to the psychiatric hospital were identified and recruited for participation in this study using a bed locator sheet (an updated record that identified the names, admission dates, cautionary ratings, and room assignments for all current inpatients). Consumers were considered eligible for participation if they had been admitted to the facility for a minimum of 5 days, were able to communicate in English, were not admitted to the facility after being adjudicated not competent to stand trial and not restorable, were not receiving competency restoration services, and were at least 18 years of age. Participants were selected in the order of their presentation on the bed locator sheets such that the first available consumer (e.g., met inclusion criteria, had not already participated or refused participation) was contacted by a research assistant for recruitment into the study presented here. Research assistants approached the identified consumers either in the day room of the facility or in their assigned rooms and asked them to meet with a research assistant regarding participation. During this meeting, consumers were verbally informed about the purpose of the study, the tasks they would be asked to complete, and of their rights as a research participant (i.e., confidentiality, right to withdraw) should they decide to participate. Additionally, risks and benefits of participation were also verbally explained. Consumers willing to participate were provided with a written informed consent form to read, and then they were given an opportunity to ask any questions they had about participation. For participants who indicated reading difficulty or an inability to read, the consent form was read aloud by a research assistant. To ensure the participant’s ability to give consent, the research assistant asked all potential participants to provide a summary, in their own

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words, regarding the information presented in the consent form. Once all questions were answered and the research assistant determined that the individual had provided informed consent, the patient was asked to sign one copy of the informed consent form. The research assistant collected the signed consent form and provided the patient with an unsigned copy of the consent form for their records. After consent was provided, the participant was provided a packet containing the demographic form, CSS-M, PICTS, and MCMI-III. They were instructed to complete the measures in the order in which they were provided to them in the packet. The demographic form was always presented first, the CSS-M and PICTS were counterbalanced to control for any sequencing effects and presented after the demographic form, and the MCMI-III was presented last. The MCMI-III was the last measure completed by the participants in an attempt to reduce attrition rates due to the length of the measure. A research assistant remained with participants while the measures were completed to enable participants to ask any questions or report any concerns that arose during the completion of the measures. For those participants who indicated difficulty reading and had the consent form read aloud to them, a research assistant also read the measures to those participants (n ⫽ 12, 12.8%) and allowed them to record their responses on the research materials. Additionally, some participants requested assistance with reading because of fatigue or difficulty with the measure, and for those participants portions of the testing were read aloud to them (n ⫽ 6, 6.4%). In addition to the packet of measures provided to the participant, the SCID-I was administered by a research assistant to approximately 20% of the sample. Administration of the SCID-I was predetermined using a random numbers table to select the packet/ participant numbers that would include the additional interview. All attempts were made to ensure that data were collected from participants during one uninterrupted session; however, this was not feasible across all participants. Interruptions ranged from short breaks (e.g., medication administration, restroom breaks, visitation, cigarette breaks) to breaks that required testing to be completed the next day (e.g., participant fatigue). Research assistants maintained a research log that documented the reason for, and the length of, all breaks/interruptions. A total of 10 participants (10.6%) completed the measures across two separate testing sessions on different days, 34 participants (36.2%) took one short break during testing, 20 participants (21.3%) took two to four breaks during testing, and 40 participants (42.6%) completed the measures without interruption. Research assistants recorded additional information regarding the start and completion times for testing, the date of testing, and any other notable or relevant information (e.g., problematic behavior, questions or problems with measures/questions). After all measures were completed, a review of each participant’s facility file was completed by a research assistant to gather additional information and to corroborate the self-report information provided by the participant. Information regarding current DSM-IV–Text Revision (DSM-IV-TR) (American Psychiatric Association, 2000) mental health diagnoses (Axis I through Axis IV and Global Assessment of Functioning scores), admission date, prescribed medications and dosages, history of psychiatric hospitalizations, and other mental health treatment received was extracted from the file.

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Data Preparation Before completing data analyses, all participant data were reviewed for incomplete/missing data or invalid profiles. The MCMI-III cannot be scored if more than 12 items were unanswered. Furthermore, MCMI-III profiles were determined to be invalid if at least two of the three improbable statements that make up the Validity Index (Scale V) are marked as true or if the base rate score on the Disclosure Scale (Scale X) is less than 34 or greater than 178 (Strack, 2008). Invalid MCMI-III profiles were removed from the analyses. There is no concrete cutoff score for determining the validity of PICTS profiles; however, a conservative T-score cutoff of 75 on either the Confusion (Cf) or Defensiveness (Df) scale was used as suggested by Walters (2006). Any PICTS profiles with T-scores that exceeded 75 on either of the validity indices were removed from further analyses. Lastly, given that the CSS-M does not have validity indicators, for participants whose PICTS data were removed because of an invalid profile, we also removed their CSS-M data from further analyses. Review of the validity indicators, as indicated above, revealed that 13 participants from the inpatient mental health sample and 26 participants from the incarcerated comparison sample produced invalid MCMI-III profiles, and 8 participants from the inpatient mental health sample and 4 from the incarcerated mental health sample failed to complete the measure such that the measure was not able to be scored. Additionally, 28 participants from the inpatient mental health sample and 29 from the incarcerated mental health sample produced invalid PICTS profiles, and 5 participants from the inpatient sample and 2 participants from the incarcerated sample failed to complete the measure such that the measure was not able to be scored. A total of 61 inpatient and 63 incarcerated PMI had valid PICTS profiles, and 73 inpatient and 64 incarcerated PMI produced valid MCMI-III profiles. All individuals that produced invalid profiles were removed from the analysis involving that measure. Lastly, assumptions of regression (i.e., the linearity, homoscedasticity, and normality of errors) were assessed before completing the regression analyses. Examination of the P-P plot indicated that the error value was not normally distributed across each value of the independent variables. To correct for this violation, a log transformation was performed on the number of prior lifetime psychiatric hospitalizations.

Results Mental Illness To examine the hypothesis that all three groups would evidence similar overall levels of psychiatric symptomatology, a one-way multivariate analysis of variance (MANOVA) procedure for each of the MCMI-III scale clusters (i.e., clinical personality patterns, severe personality pathology, clinical syndromes, severe syndromes, and modifying indices) was performed to examine group differences among the inpatient PMI with CJ involvement, inpatient PMI without CJ involvement, and the incarcerated PMI. The analysis of the clinical personality pattern scales indicated significant between-group differences, ⌳(22,248) ⫽ 1.83, p ⫽ .015. Follow-up univariate analyses indicated a significant group difference for the antisocial personality disorder (ASPD) scale, F(2,

134) ⫽ 4.149, p ⫽ .018. A Tukey post hoc analysis indicated that the inpatient PMI without CJ involvement evidenced significantly lower scores on the ASPD scale than the incarcerated PMI (p ⫽ .016). Discriminant function analysis (DFA) was conducted to determine the predictive ability of the variables in determining the group assignment of the participants. Results of DFA indicated that 56.9% of the participants were correctly classified utilizing the clinical personality pattern scales. Further, Functions 1 through 2 accounted for 25.9% (p ⫽ .015) of the between-group variance with Histrionic being the most predictive of group membership followed by Avoidant, Depressive, and Schizoid. Results of the MANOVA with the severe personality pathology scales indicated significant between-group differences, ⌳(6,262) ⫽ 3.05, p ⫽ .007; however, follow-up univariate analyses indicated no significant group differences for any of the specific scales. DFA indicated that 50.4% of the participants were correctly classified using the severe personality pathology scales. Further, Functions 1 through 2 accounted for 12.5% (p ⫽ .007) of the between-group variance with the Paranoid and Borderline scales being heavily weighted in the prediction of group assignment. As hypothesized, the results of the MANOVA with the clinical syndrome scales (⌳(14,256) ⫽ 1.27, p ⫽ .228), the severe syndrome scales (⌳(6,264) ⫽ 1.31, p ⫽ .252), and the modifying indices (⌳(6,264) ⫽ 1.18, p ⫽ .319) indicated no significant betweengroup differences. Additionally, DFA indicated that the Functions 1 through 2 accounted for an insignificant amount of variance among the groups for the clinical syndrome scales (12.5%, p ⫽ .228), severe syndrome scales (5.7%, p ⫽ .252), and the modifying indices. The results generally supported the initial hypothesis because the psychiatric presentation of PMI was similar regardless of their involvement in the CJ system with the exception that it does appear that symptoms related to ASPD may be influencing the CJ involvement of some PMI. That is, serious symptoms of Axis I diagnoses were not found to be a distinguishing feature of the two groups. The MCMI-III profile similarities and differences among the three groups are displayed in Figure 1, and effect sizes for all between-group comparisons are presented in Table 2.

Criminal Thinking To examine the hypothesis that PMI admitted to the short-term psychiatric hospital without a history of CJ involvement would evidence less criminal thinking, as measured by the PICTS and the CSS-M, than the PMI who were incarcerated or admitted to the short-term psychiatric facility with a history of CJ involvement, the various individual scales and scale clusters on the measures were compared for between-group differences using analysis of variance (ANOVA) and MANOVA procedures, as appropriate. The PICTS profiles of the inpatient PMI with CJ involvement, inpatient PMI without CJ involvement, and the incarcerated PMI were compared for between-group differences using a one-way MANOVA for each of the scale clusters (i.e., validity scales, criminal thinking scales, factor scales, content scales, and composite scales) and a one-way ANOVA for General Criminal Thinking and Fear of Change. The analysis of the General Criminal Thinking scale (thought patterns that support and reinforce engaging in criminal behavior) indicated significant between-group differences, F(2, 120) ⫽ 4.13, p ⫽ .018. A Tukey post hoc analysis indicated that the inpatient PMI without CJ involvement evidenced


181

95

Base Rate Score

85 75 65 55 45

Mental Health Facility - CJ Involvement

35

Mental Health Facility - No CJ Involvement Psychaitric Prison Schizoid Avoidant Depressive Dependent Histrionic Narcissistic Antisocial Sadistic Compulsive Negativisitic Masochistic Schizotypal Borderline Paranoid Anxiety Somatoform Bipolar:Manic Dysthymia Alcohol Dep. Drug Dep. Post Traumatic Stress Thought Disorder Major Depression Delusional Disorder


25

MCMI-III Sclaes

Figure 1. Comparison of the short-term psychiatric inpatient sample’s MCMI-III scores with an incarcerated PMI sample from Morgan et al. (2010).

significantly lower levels of General Criminal Thinking than the incarcerated PMI (p ⫽ .016). The analysis of the Fear of Change scale (apprehension and resistance toward personal and environmental change) indicated no significant between-group differences, F(2, 120) ⫽ .157, p ⫽ .855.

The MANOVA with the two composite scales indicated significant between-group differences, ⌳(4,236) ⫽ 3.17, p ⫽ .014. Follow-up univariate analyses indicated a significant group difference for the Proactive Criminal Thinking scale (criminal behavior that is planned and motivated by some expected gain), F(2, 119) ⫽

Table 2 Effect Sizes for Between-Group Comparisons on the MCMI-III Scales

Scale

Inpatient without CJ vs. Inpatient with CJ d (95% CI)

Inpatient with CJ vs. Incarcerated PMI d (95% CI)

Inpatient without CJ vs. Incarcerated PMI d (95% CI)

Disclosure Desirability Debasement Schizoid Avoidant Depressive Dependent Histrionic Narcissistic Antisocial Sadistic Compulsive Negativistic Masochistic Schizotypal Borderline Paranoid Anxiety Somatoform Bipolar: Manic Dysthymia Alcohol Dependence Drug Dependence Posttraumatic Stress Thought Disorder Major Depression Delusional Disorder

.35 (⫺.11–.81) .06 (⫺.40–.52) .10 (⫺.36–.56) .14 (⫺.32–.60) .24 (⫺.22–.70) .29 (⫺.17–.75) .22 (⫺.24–.68) .16 (⫺.30–.62) .09 (⫺.37–.55) .54 (.07–1.01) .10 (⫺.36–.56) .47 (⫺.002–.93) .02 (⫺.44–.48) .06 (⫺.40–.52 .00 (⫺.01–.01) .41 (⫺.06–.87) .53 (.06–1.00) .19 (⫺.28–.65) .08 (⫺.38–.54) .34 (⫺.13–.80) .04 (⫺.42–.50) .59 (.11–1.05) .60 (.13–1.07) .25 (⫺.21–.71) .25 (⫺.21–.71) .11 (⫺.35–.57) .37 (⫺.10–.83)

.33 (⫺.07–.73) .06 (⫺.34–.46) .17 (⫺.23–.57) .09 (⫺.31–.48) .08 (⫺.31–.48) .06 (⫺.34–.46) .31 (⫺.10–.71) .03 (⫺.37–.43) .18 (⫺.22–.58) .05 (⫺.35–.45) .37 (⫺.03–.77) .03 (⫺.37–.43) .36 (⫺.04–.76) .26 (⫺.14–.66) .05 (⫺.35–.45) .28 (⫺.12–.68) .31 (⫺.09–.71) .05 (⫺.35–.44) .30 (⫺.10–.70) .41 (.00–.81) .26 (⫺.14–.66) .01 (⫺.35–.36) .05 (⫺35–.44) .30 (⫺.10–.70) .41 (.00–.81) .26 (⫺.14–.66) .01 (⫺.35–.36)

.07 (⫺.35–.48) .02 (⫺.39–.44) .17 (⫺.25–.58) .21 (⫺.21–.62) .05 (⫺.37–.46) .03 (⫺.39–.44) .30 (⫺.12–.72) .00 (⫺.17–.18) .01 (⫺.40–.43) .55 (.13–.98) .03 (⫺.38–.45) .18 (⫺.24–.59) .01 (⫺.40–.43) .12 (⫺.29–.54) .05 (⫺.37–.47) .05 (⫺.36–.47) .44 (.02–.86) .13 (⫺.28–.55) .16 (⫺.26–.58) .28 (⫺.14–.69) .33 (⫺.09–.75) .27 (⫺.15–.69) .47 (.05–.89) .03 (⫺.39–.45) .19 (⫺.23–.60) .14 (⫺.28–.56) .31 (⫺.11–.72)

Note.

CI ⫽ confidence interval.

GROSS AND MORGAN

6.158, p ⫽ .003. A Tukey post hoc analysis indicated that the inpatient PMI without CJ involvement evidenced significantly lower scores on the Proactive Criminal Thinking scale than the incarcerated PMI (p ⫽ .002). DFA indicated that 50.8% of the participants were correctly classified using the composite scales. Further, a the composite scales accounted for a significant amount of the variance among our groups because Functions 1 through 2 accounted for 10% (p ⫽ .014) of the between-group variance with the groups differing most greatly on Proactive Criminal Thinking. The MANOVA with the eight criminal thinking scales indicated significant between-group differences, ⌳(16,226) ⫽ 1.83, p ⫽ .029. Follow-up univariate analyses indicated a significant group difference for the Cutoff scale (tendency to be emotionally reactive and a propensity to disregard or not consider negative consequences; F(2, 120) ⫽ 4.45, p ⫽ .014), Sentimentality scale (disregarding the negative impact of one’s actions or perceiving one’s actions as being beneficial to others; F(2, 120) ⫽ 3.54, p ⫽ .032), Superoptimism scale (the belief that one will be able to avoid the negative consequences of engaging in criminal activity; F(2, 120) ⫽ 4.69, p ⫽ .011), and Cognitive Indolence scale (poor problemsolving and reasoning abilities such that one takes “shortcuts”; F(2, 120) ⫽ 3.18, p ⫽ .045). Tukey post hoc analyses indicated that the inpatient PMI without CJ involvement evidenced significantly lower levels of criminal thinking than the incarcerated PMI on Sentimentality (p ⫽ .024), Superoptimism (p ⫽ .008), and Cognitive Indolence (p ⫽ .036). Additionally, the inpatient PMI without CJ involvement evidenced significantly lower levels of the Cutoff criminal thinking style when compared with both the inpatient PMI with CJ involvement (p ⫽ .026) and the incarcerated PMI (p ⫽ .020). DFA indicated that 58.5% of the participants were correctly classified using the eight criminal thinking scales. Further, the amount of variance accounted for among our groups by these scales was significant because Functions 1 through 2 accounted for 21.6% (p ⫽ .029) of the between-group variance with the groups differing most greatly on Power Orientation followed by Cutoff, Superoptimism, and Sentimentality.

The MANOVA with the five factor scales indicated significant between-group differences, ⌳(8,234) ⫽ 2.54, p ⫽ .012. Follow-up univariate analyses indicated a significant between-group difference for the Assertion/Deception scale (tendency to give one’s own needs precedence and do whatever is necessary to achieve one’s goals despite the potential harm to others; F(2, 119) ⫽ 7.945, p ⫽ .001) and the Denial of Harm scale (minimization or denial of the harm one’s behavior may have caused others; F(2, 120) ⫽ 4.018, p ⫽ .020). A Tukey post hoc analysis indicated that the inpatient PMI without CJ involvement evidenced significantly lower scores on the Denial of Harm scale than the incarcerated PMI (p ⫽ .017) and significantly lower scores on the Assertion/ Deception scale than the incarcerated PMI (p ⫽ .000) and the inpatient PMI with CJ involvement (p ⫽ .032). DFA indicated that 56.1% of the participants were correctly classified using the five factor scales. Further, Functions 1 through 2 accounted for 16.1% (p ⫽ .024) of the between-group variance with Self-Assertion/ Deception being most predicative of group assignment followed by Interpersonal Hostility and Denial of Harm. The MANOVA with the two content scales indicated significant between-group differences, ⌳(4,238) ⫽ 3.75, p ⫽ .006. Follow-up univariate analyses indicated a significant group difference for the Historical Criminal Thinking scale (having had a criminal belief system supportive of criminal behavior in the past; F(2, 120) ⫽ 7.783, p ⫽ .001). A Tukey post hoc analysis indicated that the inpatient PMI without CJ involvement evidenced significantly lower scores on the Historical Criminal Thinking scale than the incarcerated PMI (p ⫽ .000) and the inpatient PMI with CJ involvement (p ⫽ .020). DFA indicated that 54.5% of the participants were correctly classified using the content scales. Further, the content scales accounted for a significant amount of the variance between our groups because Functions 1 through 2 accounted for 11.5% (p ⫽ .006) of the between-group variance with historical criminal thinking being most predictive of group membership. The MANOVA with the validity scales (⌳(4,238) ⫽ 1.86, p ⫽ .118) indicated no significant between-group differences. See Figure 2 for PICTS

58 56 54 52 T Scores


182

50 48 46


44

Mental Health Facility - No CJ Involvement

42

Psychiatric Prison

40 38

Criminal Thinking Scale

Figure 2. Comparison of the short-term psychiatric inpatient sample’s PICTS scales with an incarcerated PMI sample from Morgan et al. (2010).



profiles of the three participant groups. DFA indicated that 52% of the participants were correctly classified using validity scales. Further, Functions 1 through 2 accounted for a nonsignificant amount of the variance among the groups because they accounted for 6% (p ⫽ .118) of the between-group variance, suggesting minimal predictive ability regarding group assignment. Additionally, effect sizes for all between-group comparisons are presented in Table 3. These results support our hypothesis that psychiatric inpatients without a history of CJ involvement would evidence less criminal thinking than incarcerated PMI and inpatients with CJ involvement. CSS-M profiles of the inpatient PMI with CJ involvement, inpatient PMI without CJ involvement, and the incarcerated PMI were compared for between-group differences using a one-way MANOVA for the individual subscales (i.e., Attitude toward the Law, Attitude toward the Court, Attitude toward Police, TLV, and ICO) and an ANOVA for the Total and the LCP score. There were no between-group differences for the Total score, F(2, 121) ⫽ 1.181, p ⫽ .310; the LCP scale, F(2, 121) ⫽ 1.835, p ⫽ .164; or the subscales, ⌳(10,234) ⫽ 1.767, p ⫽ .068. However, all three samples produced a mean Total score that exceeded the clinical threshold for the Total score (i.e., a score ⱖ19), but only the incarcerated PMI sample produced a “high” average Total score (i.e., a score ⱖ30). DFA indicated that 52.4% of the participants were correctly classified using the subscales. Further, the variables accounted for an insignificant amount of variance among our groups because Functions 1 through 2 accounted for 13.5% (p ⫽ .067) of the between-group variance. See Figure 3 for the CSS-M profiles of the three groups. Additionally, effect sizes for all between-group comparisons are presented in Table 4. Contrary to findings with the PICTS, these results from the CSS-M did not support our hypothesis that psychiatric inpatients without a history of CJ involvement would evidence less criminal thinking than incarcerated PMI and inpatients with CJ involvement.

183

Criminal Thinking and Lifetime Psychiatric Hospitalization Simultaneous linear regression was used to examine the relationship between the number of lifetime psychiatric hospitalizations (log transformed as noted above) and criminal thinking. These analyses were conducted separately for the inpatient mental health sample with and without CJ involvement. Specifically, separate simultaneous linear regression analyses were conducted with the number of lifetime hospitalizations entered as the dependent variable and the thinking style (eight scales), content (two scales), composite (two scales), and factor and special scales (five scales) as the independent variables. Separate simple linear regression analyses were conducted for the CSS-M scales (Total, Attitude toward Law, Attitude toward Police, Attitude toward Court, ICO, TLV, LCP) as individual predictors of the number of psychiatric hospitalizations. The eight criminal thinking styles accounted for approximately 54% of the variance in psychiatric hospitalizations for the inpatient sample with past CJ involvement (F(8, 26) ⫽ 2.607, p ⫽ .044, SE ⫽ .737). Lifetime psychiatric hospitalizations were found to be positively associated with the individual criminal thinking scales of Cutoff (b ⫽ 0.45, p ⫽ .002, SE ⫽ .012) and Entitlement (b ⫽ 0.30, p ⫽ .013, SE ⫽ .011) when holding the other criminal thinking scales constant. Additionally, lifetime psychiatric hospitalizations were found to be negatively associated with the individual criminal thinking scales of Mollification (b ⫽ .021, p ⫽ .033, SE ⫽ .009), Power Orientation (b ⫽ ⫺0.23, p ⫽ .008, SE ⫽ .008) and Cognitive Indolence (b ⫽ ⫺.039, p ⫽ .11, SE ⫽ .014) when holding the other thinking style scales constant. None of the other PICTS or CSS-M analyses yielded statistically significant results (p ⬍ .05). Thus, our hypothesis that criminal thinking would be positively correlated with psychiatric hospitalizations was only partially supported.

Table 3 Effect Sizes for Between-Group Comparisons on the PICTS Scales

Scale

Inpatient without CJ vs. Inpatient with CJ d (95% CI)

Inpatient with CJ vs. Incarcerated PMI d (95% CI)

Inpatient without CJ vs. Incarcerated PMI d (95% CI)

Confusion Defensiveness General Criminal Thinking Proactive Reactive Mollification Cutoff Entitlement Power Orientation Sentimentality Superoptimism Cognitive Indolence Discontinuity Problem Avoidance Interpersonal Hostility Self-Assertion/Deception Denial of Harm Current Historical Fear of Change

.36 (⫺.15–.88) .62 (.10–1.14) .60 (.08–1.18) .59 (.07–1.11) .39 (⫺.12–.91) .47 (⫺.04–.99) .64 (.12–1.16) .17 (⫺.33–.68) –.16 (⫺.35–.67) .39 (⫺.12–.91) .56 (.05–1.08) .44 (⫺.07–.96) .32 (⫺.18–.84) .18 (⫺.32–.70) .40 (⫺.11–.92) .71 (.19–1.24) .56 (.05–1.09) .22 (⫺.29–.73) .77 (.25–1.30) –.12 (⫺.39–.63)

.09 (⫺.34–.51) .31 (⫺.11–.73) .07 (⫺.35–.49) .26 (⫺.17–.68) .02 (⫺.40–.44) .09 (⫺.33–.51) .06 (⫺.36–.48) .31 (⫺.11–.73) .06 (⫺.36–.48) .17 (⫺.25–.59) .17 (⫺.25–.59) .12 (⫺.31–.54) .01 (⫺.33–.33) .02 (⫺.40–.45) .10 (⫺.32–.52) .25 (⫺.17–.67) .08 (⫺.34–.50) .04 (⫺.38–.46) .20 (⫺.23–.62) .05 (⫺.37–.47)

.27 (⫺.18–.72) .43 (⫺.02–.89) .61 (.14–1.06) .78 (.31–1.25) .38 (⫺.07–.84) .39 (⫺.07–.84) .62 (.16–1.08) .50 (.04–.96) .11 (⫺.34–.56) .62 (.15–1.07) .68 (.22–1.14) .55 (.09–1.01) .33 (⫺.12–.78) .18 (⫺.27–.63) .24 (⫺.21–.69) .89 (.41–1.35) .62 (.15–1.08) .27 (⫺.18–.72) .86 (.39–1.32) .10 (⫺.35–.55)

GROSS AND MORGAN

184 35 30

Total Score

25 20 15 10


5

Mental Health Facility - No CJ Involvement


0

Psychiatric Prison

CSS-M Scale

Figure 3. Comparison of CSS-M scale scores for short-term psychiatric inpatients with an incarcerated PMI sample from Morgan et al. (2010).

Discussion The purpose of this study was to examine the differences between PMI with and without CJ involvement with regards to psychiatric presentation and criminal thinking. Similar Morgan et al. (2010) and Wolff et al. (2011) results indicated that PMI with CJ involvement present similarly to incarcerated PMI with regards to psychiatric symptomatology and criminal thinking. Furthermore, PMI without CJ involvement are distinguished from incarcerated PMI by displaying lower levels of criminal thinking and lower levels of psychiatric symptomatology directly related to criminal risk (i.e., ASPD), but they present similarly with regards to other psychiatric symptoms. These results support Skeem, Manchak, and Peterson (2011) estimate that 9 in 10 persons with mental illness who become involved in the CJ system do so for reasons unrelated to mental illness (incarcerated PMI and PMI with CJ involvement evidenced psychiatric symptoms similar to PMI without CJ involvement). The CJ involvement of some PMI is most likely due to reasons related to criminal risk (e.g., criminal thinking, antisocial personality) because the incarcerated PMI evidenced higher scores on several criminal thinking scales (i.e., General Criminal Thinking, Proactive Criminal Thinking, Cutoff, Cognitive Indolence, Sentimentality, Superoptimism, Assertion/ Deception, Denial of Harm) and more severe levels of symptoms

related to ASPD than the inpatient PMI group without CJ involvement. Said differently, it appears that some PMI that are CJ involved present with features known to be associated with criminality (see Andrews & Bonta, 2006), in addition to their mental illness symptomatology, that increase their propensity to engage in crime. Therefore, this group of PMI appears to be engaging in criminal behavior because of criminal risk factors and not as a result of unmanaged mental health symptoms. These findings suggest that PMI with CJ involvement necessitate treatment that goes beyond traditional mental health treatment by integrating criminal risk factors as treatment foci. The increased levels of criminal thinking and criminally related psychiatric symptomatology for this population are paramount treatment considerations because Andrews and Bonta (2006) identified criminal thinking as one of the “Central Eight” risk factors that increase the likelihood of future engagement in criminal behavior. Furthermore, criminal risk factors can be discussed alongside mental health concerns as concurrent treatment foci (Epperson et al., 2011). Integrated treatment programs could yield a potential reduction in recidivism (psychiatric and criminal) via improved treatment modalities, thus improving the lives and functioning of PMI regardless of placement and thereby increasing community safety (reduced crime) and reducing public health concerns (re-

Table 4 Effect Sizes for Between-Group Comparisons on the CSS-M Scales

Scale Attitudes Toward the Law Attitudes Toward the Court Attitudes Toward the Police Law-Court-Police Tolerance for Law Violations Identification with Criminal Others Total

Inpatient without CJ vs. Inpatient with CJ vs. Inpatient without CJ vs. Inpatient with CJ Incarcerated PMI Incarcerated PMI d (95% CI) d (95% CI) d (95% CI) .37 (⫺.13–.88) –.15 (⫺.35–.66) .41 (⫺.09–.92) .22 (⫺.28–.73) .11 (⫺.38–.61) .20 (⫺.29–.70) .23 (⫺.27–.74)

.11 (⫺.32–.53) –.35 (⫺.08–.77) –.29 (⫺.14–.72) –.20 (⫺.22–.63) –.02 (⫺.40–.44) .01 (⫺.41–.43) –.12 (⫺.31–.54)

–.19 (⫺.25–.63) –.22 (⫺.23–.66) .66 (.21–1.11) .42 (⫺.02–.86) –.13 (⫺.31–.57) .22 (⫺.23–.66) .34 (⫺.10–.78)



duced overuse of medical and psychiatric services). On the basis of the results of this study as well as those of Morgan et al. (2010) and Wolff et al. (2011), criminal thinking is one such treatment need. Treatment programs that address criminogenic (e.g., substance abuse, emotions management, criminal attitudes, criminal associates) and psychiatric (e.g., medication compliance, mental illness awareness, social-skills, problem solving) needs of offenders will prove most efficacious in terms of reducing criminal behavior and mental health symptomatology (Moran & Hodgins, 2004; Morgan, Kroner, Mills, & Bauer, 2011). The need for integrated treatment is highlighted by findings that empirically supported mental health treatment programs show no measurable effects on criminal involvement even when significant improvements are made in psychiatric recovery (Calsyn, Yonker, Lemming, Morse, & Klinkenberg, 2005; Morrissey, Meyer, & Cuddeback, 2007). However, there may be a reciprocal relationship such that criminal risk factors may influence or hinder psychiatric outcomes (e.g., medication compliance, treatment adherence, psychiatric hospitalizations). Incarcerated PMI were found to have psychiatric hospitalization rates that were 3 times higher than PMI who were not CJ involved (Fisher et al., 2002), suggesting a unique feature—potentially criminal thinking—that may increase the potential for psychiatric hospitalization. The results of the study indicate that high levels of criminal thinking are positively associated with the number of psychiatric hospitalizations for PMI with CJ involvement. However, results of this study also indicate that PMI that are incarcerated or have a history of CJ involvement may not be any more resistant to treatment when compared with PMI without CJ involvement because of the lack of between-group differences on the Fear of Change scale on the PICTS. Through the identification of the distinguishing features between PMI with and without CJ involvement, this study has most notably elucidated the specific treatment needs of PMI with CJ involvement. However, with future research, the results have potential implications for offender “type” classification, thus identifying the most appropriate treatment and CJ route (e.g., mental health or drug court, Forensic Assertive Community Treatment, general mental health services). Despite the importance of the findings presented here, this study is not without limitations. Information regarding the participant’s history of CJ involvement was gathered via self-report and was not corroborated with other sources (e.g., criminal records). However, this concern is lessened knowing that individuals involved in the CJ system generally accurately report their criminal histories (Kroner, Mills, & Morgan, 2006). The study would have benefited by the inclusion of a community sample to use as a control group. Additionally, the possibility of sampling bias could not be explored because data (e.g., demographic) were not gathered from those who chose not to participate (n ⫽ 66, 41.25%); thus, they could not be compared to those who participated in the study. Lastly, the distinction made between the inpatient PMI with and without CJ involvement was done based on the criteria of whether the participant had been convicted of a crime. This could be a potential explanation for why there were no significant differences found between these two groups similar to those differences found between the incarcerated PMI and the inpatient PMI without CJ involvement. Future studies should examine the influence of criminal thinking across other types of contact with the CJ system (e.g., arrests, charges).

185

Nevertheless, the results of this study are informative by showing that PMI with CJ involvement, specifically those who are incarcerated, present with more criminal thinking and psychiatric symptoms related to criminal behavior. Despite this, work remains to be done. Future research should aim to expand this current line of research by evaluating the relationship between criminal thinking and the number of psychiatric hospitalizations with larger and more diverse samples. This line of research should be replicated to include the examination of other psychiatric outcome variables (e.g., recovery, treatment engagement) that may be affected by criminogenic factors. Additionally, criminal thinking and psychiatric symptomatology should be assessed across various populations (e.g., different correctional and psychiatric samples) and facility types (e.g., jail, federal prison) with various different measures of psychiatric symptomatology and criminogenic factors. Doing so would provide valuable information regarding the generalizability of the results found in this study. Lastly, including the examination of other criminal risk factors (e.g., substance use, lack of prosocial leisure activities, family conflict), in addition to criminal thinking, would provide more detailed information about the criminal treatment needs of PMI.

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Received January 26, 2012 Revision received August 20, 2012 Accepted August 24, 2012 䡲