Literature DB >> 31169067

Predicting Sexual Recidivism.

Turgut Ozkan1, Stephen J Clipper2, Alex R Piquero3, Michael Baglivio4, Kevin Wolff5.   

Abstract

The current study focuses on adolescents with sex offense histories and examines sexual reoffending patterns within 2 years of a prior sex offense. We employed inductive statistical models using archival official records maintained by the Florida Department of Juvenile Justice (FDJJ), which provides social, offense, placement, and risk assessment history data for all youth referred for delinquent behavior. The predictive accuracy of the random forest models is tested using receiver operator characteristic (ROC) curves, the area under the curve (AUC), and precision/recall plots. The strongest predictor of sexual recidivism was the number of prior felony and misdemeanor sex offenses. The AUC values range between 0.71 and 0.65, suggesting modest predictive accuracy of the models presented. These results parallel the existing literature on sexual recidivism and highlight the challenges associated with predicting sex offense recidivism. Furthermore, results inform risk assessment literature by testing various factors recorded by an official institution.

Keywords:  juvenile sex offender recidivism; machine learning; random forests; sexual recidivism

Mesh:

Year:  2019        PMID: 31169067     DOI: 10.1177/1079063219852944

Source DB:  PubMed          Journal:  Sex Abuse        ISSN: 1079-0632


  3 in total

1.  Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages.

Authors:  Lei Zhang; Jason J Ong; Xianglong Xu; Christopher K Fairley; Eric P F Chow; David Lee; Ei T Aung
Journal:  Sci Rep       Date:  2022-05-24       Impact factor: 4.996

2.  Echoing Mechanism of Juvenile Delinquency Prevention and Occupational Therapy Education Guidance Based on Artificial Intelligence.

Authors:  Fang Hou
Journal:  Occup Ther Int       Date:  2022-09-29       Impact factor: 1.565

Review 3.  Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction.

Authors:  Guido Vittorio Travaini; Federico Pacchioni; Silvia Bellumore; Marta Bosia; Francesco De Micco
Journal:  Int J Environ Res Public Health       Date:  2022-08-25       Impact factor: 4.614

  3 in total

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