PDF(Pubmed)
Abstract：
Child maltreatment is a widespread problem with significant costs for both victims and society. In this retrospective cohort study, we develop predictive risk models using Danish administrative data to predict removal decisions among referred children and assess the effectiveness of caseworkers in identifying children at risk of maltreatment. The study analyzes 195,639 referrals involving 102,309 children Danish Child Protection Services received from April 2016 to December 2017. We implement four machine learning models of increasing complexity, incorporating extensive background information on each child and their family. Our best-performing model exhibits robust predictive power, with an AUC-ROC score exceeding 87%, indicating its ability to consistently rank referred children based on their likelihood of being removed. Additionally, we find strong positive correlations between the model\'s predictions and various adverse child outcomes, such as crime, physical and mental health issues, and school absenteeism. Furthermore, we demonstrate that predictive risk models can enhance caseworkers\' decision-making processes by reducing classification errors and identifying at-risk children at an earlier stage, enabling timely interventions and potentially improving outcomes for vulnerable children.
摘要：
虐待儿童是一个普遍的问题，受害者和社会都付出了巨大的代价。在这项回顾性队列研究中，我们使用丹麦的行政数据开发了预测风险模型,以预测转诊儿童中的移除决定,并评估个案工作者在识别有虐待风险儿童方面的有效性.该研究分析了从2016年4月至2017年12月收到的涉及102,309名丹麦儿童保护服务的195,639名转介。我们实现了四种越来越复杂的机器学习模型，纳入每个孩子及其家庭的广泛背景信息。我们表现最好的模型表现出强大的预测能力，AUC-ROC得分超过87%，表明其能够根据被移除的可能性对转诊儿童进行一致排名。此外,我们发现模型的预测与各种不良儿童结局之间存在很强的正相关关系，比如犯罪，身心健康问题,和学校旷工。此外，我们证明，预测风险模型可以通过减少分类错误和在较早阶段识别风险儿童来增强个案工作者的决策过程，能够及时干预，并可能改善弱势儿童的结果。
