Exploring latent classes of adverse childhood experiences and their impact on mental health outcomes among female college students: A structural equation modeling approach

Abstract Context: Adverse childhood experiences (ACEs) are found to be a major public health concern causing physical, psychological, and emotional harm. Studies show that exposure to a single ACE has the likelihood of causing major negative outcomes, while exposure to four or more ACEs causes greater negative consequences during adulthood. Aims: To explore the different class structures of ACEs and their association with sociodemographic factors, resilience, and psychological distress among female college students using latent class analysis (LCA) and structural equation modeling (SEM) approach. Settings and Design: The current study adopted a quantitative approach with a non-probability judgment sampling (n = 387). Subjects and Methods: A descriptive empirical method was used with an ex post facto research design. Statistical Analysis Used: The study used LCA and SEM analysis to substantiate the objectives of the study. Results: From the results, four classes being moderate childhood trauma (Class 1), mild childhood trauma (Class 2), severe childhood trauma (Class 3), and no childhood trauma (Class 4) were identified which were labeled on the basis of the probability composition of the different early life adversities. Depression ( F = 7.69, P < 0.001), anxiety ( F = 13.18, P < 0.001), and stress ( F = 8.90, P < 0.001) were found to significantly vary across the classes, with severe childhood trauma participants having the highest level of psychological distress, and lowest level was found among the no childhood trauma participants. Conclusions: The study has identified four distinct latent classes with ACEs, each showing differences in the probability of the various demographic characteristics as well as psychological distress. Thus, clinicians can tailor interventions targeting specific trauma profiles.