Predictive risk modeling for child maltreatment detection and enhanced decision-making: Evidence from Danish administrative data.

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

SUBMITTER: Rosholm M 

PROVIDER: S-EPMC11236184 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature