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预测性再犯

Predicting Sexual Recidivism.

作者信息

Ozkan Turgut, Clipper Stephen J, Piquero Alex R, Baglivio Michael, Wolff Kevin

机构信息

Santa Clara University, CA, USA.

The University of Alabama, Tuscaloosa, USA.

出版信息

Sex Abuse. 2020 Jun;32(4):375-399. doi: 10.1177/1079063219852944. Epub 2019 Jun 6.

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.

摘要

当前的研究聚焦于有性犯罪历史的青少年,并考察前一次性犯罪后两年内的性再犯模式。我们使用归纳统计模型,利用佛罗里达州少年司法部(FDJJ)维护的档案官方记录,该部门提供了所有因违法犯罪行为被转介的青少年的社会、犯罪、安置和风险评估历史数据。使用受试者工作特征(ROC)曲线、曲线下面积(AUC)和精确率/召回率图来测试随机森林模型的预测准确性。性再犯的最强预测因素是先前重罪和轻罪性犯罪的数量。AUC值在0.71至0.65之间,表明所呈现模型的预测准确性一般。这些结果与关于性再犯的现有文献一致,并突出了预测性犯罪再犯所面临的挑战。此外,研究结果通过测试官方机构记录的各种因素,为风险评估文献提供了参考。

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