RT Article T1 Predicting Posttraumatic Stress Disorder Among Survivors of Recent Interpersonal Violence JF Journal of interpersonal violence VO 37 IS 13/14 A1 Morris, Matthew C. A2 Sanchez-Sáez, Francisco A2 Bailey, Brooklynn A2 Hellman, Natalie A2 Williams, Amber A2 Schumacher, Julie A. A2 Rao, Uma LA English YR 2022 UL https://krimdok.uni-tuebingen.de/Record/1884267343 AB A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs. K1 PTSD K1 Assault K1 interpersonal violence K1 Longitudinal K1 Prediction K1 Women DO 10.1177/0886260520978195