Forecasting Recidivism in Mentally Ill Offenders Released From Prison.

Abstract

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.