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.2023 Jan 19;18(1):e0278194.
doi: 10.1371/journal.pone.0278194. eCollection 2023.

Mental health disorders and recidivism among incarcerated adult offenders in a correctional facility in South Africa: A cluster analysis

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Mental health disorders and recidivism among incarcerated adult offenders in a correctional facility in South Africa: A cluster analysis

Kwanele Shishane et al. PLoS One..

Abstract

The contribution of mental illness, substance use, and appetitive aggression to recidivism has significant policy and practice implications. Offenders with untreated mental illness have a higher recidivism rate and a greater number of criminogenic risk factors than those without mental illness. Previous research has demonstrated that the likelihood of appetitive aggression increases in violent contexts where individuals perpetrate aggressive acts. Using the Ecological Systems Theory, this study investigated the association between mental health disorders and recidivism among incarcerated adult offenders in South Africa, and the intervening role of appetitive aggression and substance use. Using a cross-sectional quantitative research design, a sample of 280 incarcerated male and female adult offenders aged 18-35 with no known psychiatric disorders were sampled at a correctional facility in South Africa. The re-incarceration rate, mental health disorders, substance use, and appetitive aggression symptomology were assessed using the Hopkins symptoms checklist, the CRAFFT measure of substance use, and the appetitive aggression scale. Findings indicate a 32.4% recidivism rate (n = 82). Cluster analysis indicated that the combination of anxiety, depression, substance use, and appetitive aggression increased the likelihood of recidivism. Appetitive aggression median differences between clusters 2 and 3 played a key role in distinguishing recidivism risk among recidivist and non-recidivist participants. Chi-square analysis highlighted group differences in education levels among the established clusters [x2 (3, n = 217) = 12.832, p = .005, which is < .05] as well as group differences in the type of criminal offence [x2 (3, n = 187) = 24.362, p = .000, which is < .05] and cluster membership. Combined factors that increase the likelihood of recidivism provide a typology for classifying offenders based on particular recidivism risk determinants, which offers insights for developing tailored interventions that address a combination of factors.

Copyright: © 2023 Shishane et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Crime cycle.
Fig 2
Fig 2. A hypothesized model of the central study variables.
Fig 3
Fig 3. Silhouette measure of cohesion and separation diagram.
Fig 4
Fig 4. Cluster 1 “low risk” (non-recidivist, n = 98) whiskers box plot of values associated with each variable.
Fig 5
Fig 5. Cluster 2 “high risk” (non-recidivist, n = 61) whiskers box plot of values associated with each variable.
Fig 6
Fig 6. Cluster 3 “high risk” (recidivist, n = 39) whiskers box plot of values associated with each variable.
Fig 7
Fig 7. Cluster 4 “low risk” (recidivist, n = 32) whiskers box plot of values associated with each variable.
Fig 8
Fig 8. Chi-square test of cluster membership based on education level.
Fig 9
Fig 9. Chi-square test of cluster membership based on offense type.
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References

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The authors have no financial obligations to disclose. This study was conducted as part of author KS's doctoral studies, whose tuition was funded by doctoral scholarship awards from the National Institute for the Humanities and Social Sciences and the Harry Crossley Research Foundation. There was no financial obligation attached to these scholarships.

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