Deciphering acute stress disorder symptoms in firefighters: Embracing the network analysis perspective

BACKGROUND: Firefighters face high traumatic exposure during their work, increasing the risk of acute stress disorder (ASD). As a transient but critical early phase following trauma, ASD plays a pivotal role in the onset and progression of subsequent trauma-related psychological disorders. However, the symptomatic characteristics and psychological mechanisms of ASD in firefighter populations remain unclear. This study aimed to explore ASD in firefighters from a network analysis perspective, identifying potential targets to prevent further deterioration. METHODS: A total of 1085 firefighters who were actively engaged in flood rescue operations were included in this study. Two network construction methodologies, the regularized partial correlation network (RPCN) and directed acyclic graph (DAG), were employed to perform network analysis. RESULTS: For the RPCN, A3 "Things seem unreal" and A16 "Difficulty concentrating" had the highest expected influence (EI) values and served as the central symptoms. Regarding the DAG, the results indicated that A6 "Intrusive memories" had the highest probabilistic priority and was identified as the potential activation symptom of the network. Moreover, A3 "Things seem unreal" and A16 "Difficulty concentrating" were located in the second layer of the DAG, mediating the connections between the uppermost node A6 and other downstream nodes. CONCLUSION: The convergence of DAG and RPCN findings provides a holistic model of ASD in firefighter populations. Our findings offer a network-informed framework for understanding ASD psychopathology in firefighters, identifying derealization, distractibility, and intrusive memories as theoretically grounded targets for early psychological intervention.