This study examined the extent to which the contingencies risk framework (consisting of dispositional, historical, contextual, and clinical domains) predicted detected recidivism (i.e., arrest or conviction). Secondary data were extracted on 413 prisoners who underwent a psychiatric evaluation, were assessed on the risk domains, and followed up over 20 years. There were 273 (66.1%) cases of recidivism for serious offenses (e.g., assaults). Criminal career outcomes examined included: years to and the incidence of recidivism. Statistics showed that chance classification of the incidence of recidivism was 21% more accurate for the recursive partitioning than the bilinear model. These results are consistent with the contingencies risk framework, support its use over linear models, and highlight its predictive utility.
- Recursive partitioning