Business Commons^™

Developing A Risk Model To Target High-Risk Preventive Interventions For Sexual Assault Victimization Among Female U.S. Army Soldiers, Amy E. Street, Anthony J. Rosellini, Robert J. Ursano, Steven G. Heeringa, Eric D. Hill, John Monahan, James A. Naifeh, Maria V. Petukhova, Ben Y. Reis, Nancy A. Sampson, Paul D. Biese, Murray B. Stein, Alan M. Zaslavsky, Ronald C. Kessler

Faculty Publications

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. …