Individual factors associated with recidivism among mental health court program clients

Loong, Desmond; Barnsley, Jan; Aubry, Tim; Dewa, Carolyn S.

doi:10.1016/j.ijlp.2020.101651

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…Many existing research on incarceration-diversion program has focused on outcome prediction and the identification of predictors for successful completion (Loeb, Waung, and Sheeran 2015;Verhaaff and Scott 2015;Loong et al 2021). Studies have also emphasized the role of probation/case officers, showing that reduced caseloads can significantly reduce recidivism rates (Burrell 2006;Jalbert et al 2010;Jalbert and Rhodes 2012), underscoring the benefits of proper staffing and manageable caseloads in enhancing program effectiveness and supervision.…”

Section: Background and Related Workmentioning

confidence: 99%

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management

Li,

Castellanos,

Shi

et al. 2024

AAAI

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Incarceration-diversion programs have proven effective in reducing recidivism. Accurate prediction of the number of individuals with different characteristics in the program and their program outcomes based on given eligibility criteria is crucial for successful implementation, because this prediction serves as the foundation for determining the appropriate program size and the consequent staffing requirements. However, this task poses challenges due to the complexities arising from varied outcomes and lengths-of-stay for the diverse individuals in incarceration-diversion programs. In collaboration with an Illinois government agency, we develop a framework to address these issues. Our framework combines ML and queueing model simulation, providing accurate predictions for the program census and interpretable insights into program dynamics and the impact of different decisions in counterfactual scenarios. Additionally, we deploy a user-friendly web app beta-version that allows program managers to visualize census data by counties and race groups. We showcase two decision support use cases: Changing program admission criteria and launching similar programs in new counties.

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Section: Background and Related Workmentioning

confidence: 99%

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management

Li,

Castellanos,

Shi

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

Mental Illness, Substance Use, and Co-Occurring Disorders among Jail Inmates: Prevalence, Recidivism, and Gender Differences

2022

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The Influence of Homelessness on the Relationship Between Clinical Risk and Protective Factors With Violent Recidivism

Francke,

Bogaerts,

Janković

2023

Crime & Delinquency

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In this study, we investigated the potential moderating role of instability in living arrangements in the association between 14 clinical factors of the Historical Clinical Future Revised (HKT-R) and 5-year violent recidivism. The sample included 316 male forensic psychiatric patients who were unconditionally released from Dutch forensic psychiatric institutions between 2004 and 2008. Results showed that instability in living arrangements did not moderate the association between the clinical factors and violent recidivism, however, it directly and positively predicted (OR = 3.28) violent recidivism. Likewise, hostility (OR = 4.36) and addiction (OR = 1.65) were directly and positively related to violent recidivism, while psychotic symptoms (OR = 0.25) were inversely related.

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Individual factors associated with recidivism among mental health court program clients

Cited by 3 publications

References 57 publications

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management

Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management

Mental Illness, Substance Use, and Co-Occurring Disorders among Jail Inmates: Prevalence, Recidivism, and Gender Differences

The Influence of Homelessness on the Relationship Between Clinical Risk and Protective Factors With Violent Recidivism

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