Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers.

ABSTRACT: Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the

SUBMITTER: Street AE 

PROVIDER: S-EPMC5279920 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature