Predicting Probation Supervision Violations

The task of risk assessment is a central feature of probation work and a core activity of probation officers. Risk assessment forms the basis for subsequent interventions and management of offenders so that the likelihood of reoffending is reduced. A primary difficulty for probation workers is the ability to predict the risk of probation violations which could facilitate prevention. The main objective of the present study was to investigate the value of the 61-item Dutch diagnostic and risk assessment tool Recidivism Assessment Scales (RISc) with respect to predicting probation supervision violations of male probationers (N = 14,363). Because all RISc assessments included in the study were completed before the start of the supervision period, they could not have been influenced by behavior of the offenders or other circumstances during this period. It was found that the predictive accuracy of the RISc, with regard to supervision violation, was supported. All RISc subscales and the total score significantly predicted probation supervision violation. The AUC demonstrating the strength of the relationship of the RISc total score (AUC = .70) is satisfactory. Logistic regression analyses resulted in a fitting model, demonstrating that a selection of only 17 items from the total of 61 RISc items was sufficient to predict probation violation while preserving predictive accuracy (AUC = .73). For one of the possible cut-off sum scores used to select groups at high risk for probation violation, it was shown that is possible to double the percentage of correctly identified future violators when compared to the base rate of probation violation.