Understanding posttraumatic stress trajectories in adolescent females: A strength-based machine learning approach examining risk and protective factors including online behaviors

Abstract

AbstractHeterogeneity in the course of posttraumatic stress symptoms (PTSS) following a major life trauma such as childhood sexual abuse (CSA) can be attributed to numerous contextual factors, psychosocial risk, and family/peer support. The present study investigates a comprehensive set of baseline psychosocial risk and protective factors including online behaviors predicting empirically derived PTSS trajectories over time. Females aged 12–16 years (N= 440); 156 with substantiated CSA; 284 matched comparisons with various self-reported potentially traumatic events (PTEs) were assessed at baseline and then annually for 2 subsequent years. Latent growth mixture modeling (LGMM) was used to derive PTSS trajectories, and least absolute shrinkage and selection operator (LASSO) logistic regression was used to investigate psychosocial predictors including online behaviors of trajectories. LGMM revealed four PTSS trajectories: resilient (52.1%), emerging (9.3%), recovering (19.3%), and chronic (19.4%). Of the 23 predictors considered, nine were retained in the LASSO model discriminating resilient versus chronic trajectories including the absence of CSA and other PTEs, low incidences of exposure to sexual content online, minority ethnicity status, and the presence of additional psychosocial protective factors. Results provide insights into possible intervention targets to promote resilience in adolescence following PTEs.