Forecasting Recidivism in Mentally Ill Offenders Released From Prison

Little research has focused on assessing the risk of mentally ill offenders (MIOs) released from state prisons. Here we report findings for 333 mentally ill offenders released from Washington State prisons. Logistic regression identified sets of variables that forecasted felony and violent reconvict...

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Bibliographic Details
Published in:Law and human behavior 2004-04, Vol.28 (2), p.133-155
Main Authors: Gagliardi, Gregg J, Lovell, David, Peterson, Paul D, Jemelka, Ron
Format: Article
Language:English
Subjects:
Adult
Behavior
Crime - prevention & control
Crime - psychology
Criminal offenses
Criminals
Ex-convicts
Felony offenses
Female
Forecasting
Human
Humans
Imprisonment
Liability insurance
Logistic Models
Logistic regression
Male
Mental disorders
Mental Disorders - diagnosis
Mental health care
Mental illness
Mentally Ill Offenders
Mentally Ill Persons - psychology
Prisoners
Prisons
Recidivism
Recidivism prediction
Recurrence
Regression analysis
Risk Factors
Studies
Violent crimes
Washington
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Summary:Little research has focused on assessing the risk of mentally ill offenders (MIOs) released from state prisons. Here we report findings for 333 mentally ill offenders released from Washington State prisons. Logistic regression identified sets of variables that forecasted felony and violent reconviction as accurately as state-of-the-art risk assessment instruments. Sums of simple recoded versions of these variables predicted reoffense as well as complex logistic regression equations. Five of these 9 variables were found to be relative protective factors. Findings are discussed in terms of the value of stock correctional variables in forecasting risk, the need to base actuarial risk assessments on local data, the importance of protective factors in assessing MIO risk, and the need for dynamic, situational, and clinical variables that can further sharpen predictive accuracy of emergent risk in the community.
ISSN:0147-7307
1573-661X
DOI:10.1023/B:LAHU.0000022319.03637.45