RESUMO
OBJECTIVE: Mental distress and decreased functionality are highly prevalent after collective traumas. Survivors' ability to function is worsened by untreated distress and unmet needs related to job security and a sense of belonging. We aim to identify groups of people using a latent class analysis based on multiple dimensions of subjective experiences after a massive urban violent event. METHOD: This cross-sectional internet-based study included 1,305 Lebanese adults 4 months after the Beirut Port explosion. Emotions, attitudes, and needs were assessed using the iCode software, a novel approach measuring explicit subjective answers and implicit reaction time to various statements. RESULTS: Responses revealed alarming levels of distress with 75%-80% of participants feeling anxious. Latent class analysis differentiated three groups on initial dimensions derived from the principal component analysis. The first group included those with the most intense emotional distress and intrusive thoughts. They were younger, had higher job worries, and wanted to leave the country. The second group was equally distressed, with marked intrusion and avoidance yet with faith and community resilience buffering negative emotionality. The last group was less distressed with a marked sense of community. CONCLUSION: Clustering people based on emotional experiences, needs, and resources might offer additional benefits to traditional assessments that fail to detect the variability of vulnerability to mass violence within seemingly homogeneous samples. Integrating implicit and explicit responses also helps with a rapid classification, providing insight to subjective attitudes postcollective trauma. The study mostly provides suggestions for targeted outreach to at-risk subpopulations to better foster resilience in unstable environments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
