Publications by authors named "Philip D Howard"

Existing evidence suggests that offenders tend not to specialize in sexual offending in general but that there is some specialization in particular types of sexual offending. This study examined the sexual histories and reoffending of a large, national data set of offenders convicted of a sexual offense and managed in England and Wales by the National Offender Management Service (N = 14,804). The study found that specialization in sexual offending compared to nonsexual offending was most evident for offenders with convictions for accessing indecent images.

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Recent studies of multiwave risk assessment have investigated the association between changes in risk factors and violent recidivism. This study analyzed a large multiwave data set of English and Welsh offenders (N = 196,493), assessed in realistic correctional conditions using the static/dynamic Offender Assessment System (OASys). It aimed to compare the predictive validity of the OASys Violence Predictor (OVP) under mandated repeated assessment and one-time initial assessment conditions.

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This study examined the predictive validity of an actuarial risk-assessment tool with convicted sexual offenders in England and Wales. A modified version of the RM2000/s scale and the RM2000 v and c scales (Thornton et al., 2003) were examined for accuracy in predicting proven sexual violent, nonsexual violent, and combined sexual and/or nonsexual violent reoffending in a sample of sexual offenders who had either started a community sentence or been released from prison into the community by March 2007.

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Some recent articles have proposed that the confidence interval for the predicted outcome of a single case can be used to describe the predictive accuracy of risk assessments (Hart et al. Br J Psychiat 190:60-65, 2007b; Cooke and Michie, Law Hum Behav 2009). Given that the confidence intervals for an individual prediction are very large, Cooke and colleagues have questioned the wisdom of applying recidivism rates estimated from group data to single cases.

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