Mulder Eva, Brand Eddy, Bullens Ruud, van Marle Hjalmar
1 Erasmus University Medical Center, Rotterdam, Netherlands.
2 National Agency of Correctional Institutions, the Hague, Netherlands.
Int J Offender Ther Comp Criminol. 2019 May;63(6):819-836. doi: 10.1177/0306624X10387518.
The aim of this study was to identify subgroups of serious juvenile offenders on the basis of their risk profiles, using a data-driven approach. The sample consists of 1,147 of the top 5% most serious juvenile offenders in the Netherlands. A part of the sample, 728 juvenile offenders who had been released from the institution for at least 2 years, was included in analyses on recidivism and the prediction of recidivism. Six subgroups of serious juvenile offenders were identified with cluster analysis on the basis of their scores on 70 static and dynamic risk factors: Cluster 1, antisocial identity; Cluster 2, frequent offenders; Cluster 3, flat profile; Cluster 4, sexual problems and weak social identity; Cluster 5, sexual problems; and Cluster 6, problematic family background. Clusters 4 and 5 are the most serious offenders before treatment, committing mainly sex offences. However, they have significantly lower rates of recidivism than the other four groups. For each of the six clusters, a unique set of risk factors was found to predict severity of recidivism. The results suggest that intervention should aim at different risk factors for each subgroup.
本研究的目的是采用数据驱动的方法，根据严重青少年罪犯的风险特征来识别亚组。样本包括荷兰最严重的5%青少年罪犯中的1147人。样本中的一部分，即728名已从教养机构释放至少2年的青少年罪犯，被纳入累犯及累犯预测分析。基于70个静态和动态风险因素的得分，通过聚类分析确定了严重青少年罪犯的六个亚组：第1组，反社会人格；第2组，惯犯；第3组，平淡型；第4组，性问题与社会认同感薄弱；第5组，性问题；第6组，问题家庭背景。第4组和第5组在治疗前是最严重的罪犯，主要实施性犯罪。然而，他们的累犯率明显低于其他四组。对于六个聚类中的每一个，都发现了一组独特的风险因素来预测累犯的严重程度。结果表明，干预应针对每个亚组的不同风险因素。
