The Prediction of Criminal Recidivism Using Routinely Available File Information

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FRIES, Diana, Astrid ROSSEGGER, Jérôme ENDRASS, Jay P. SINGH, 2013. The Prediction of Criminal Recidivism Using Routinely Available File Information. In: International Journal of Psychological Research. 6(2), pp. 8-14. ISSN 2011-2084. eISSN 2011-7922

@article{Fries2013Predi-38034, title={The Prediction of Criminal Recidivism Using Routinely Available File Information}, year={2013}, number={2}, volume={6}, issn={2011-2084}, journal={International Journal of Psychological Research}, pages={8--14}, author={Fries, Diana and Rossegger, Astrid and Endrass, Jérôme and Singh, Jay P.} }

Rossegger, Astrid 2017-03-17T09:29:17Z Endrass, Jérôme 2013 The Prediction of Criminal Recidivism Using Routinely Available File Information Fries, Diana 2017-03-17T09:29:17Z Singh, Jay P. Objective<br /> The aim of the present study was to cross-validate the investigation of Buchanan and Leese (2006) into the prediction of criminal recidivism.<br /><br /> Method<br /> The sample comprised offenders in the criminal justice system of the Canton of Zürich – Switzerland, who were discharged to the community. Participants were followed, and evidence of subsequent charges and convictions for both general and serious recidivism was investigated at fixed periods of 2.5, 6.5, and 10.5 years. The predictive validity of socio-demographic, criminal history, and legal class information was assessed using logistic regression as well as log-likelihood, receiver operating characteristic curve, and contingency analyses.<br /><br /> Results<br /> A multivariable model including age and criminal history information was found to produce the highest rates of predictive validity for general and serious recidivism.<br /><br />Conclusion<br />Information regularly accessible in forensic practice may be able to guide clinicians as to the recidivism risk level of their patients. eng Singh, Jay P. Fries, Diana Rossegger, Astrid Endrass, Jérôme

Dateiabrufe seit 17.03.2017 (Informationen über die Zugriffsstatistik)

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