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Article Abstract
Objective: Network approaches to the pandemic-related psychopathology could help identify the effective targets for interventions in adolescents. However, previous studies involved only limited forms of psychopathology related to stress and trauma and relied on the traditional centrality analysis. This study aimed to identify more effective intervention targets of pandemic-related psychopathological symptoms in a large-scale sample of adolescents using a newly developed network-based simulation intervention technique.
Method: Data were yielded from an online survey in China conducted 3 months after the first wave of the pandemic, and participants endorsing at least one pandemic-related psychopathological symptom measured with the Global Psychotrauma Screen for Teenagers ( = 32,932) were included in the analyses. Network structure was estimated using the Ising model. Simulation interventions were implemented using the NodeIdentifyR algorithm. In the NodeIdentifyR algorithm, alleviating or aggravating interventions were implemented by decreasing or increasing the probability of a symptom being present.
Results: Psychopathological symptoms were connected tightly, and depressed mood demonstrated the highest strength centrality. The NodeIdentifyR algorithm indicated that restlessness had the strongest projected effect on decreasing the sum score after alleviating interventions, and depressed mood had the strongest projected effect on increasing the sum score after aggravating interventions.
Conclusions: Adolescents exposed to the pandemic would experience a range of interacted psychopathological symptoms and depressed mood, and restlessness might serve as a central node in activating and maintaining the symptoms, respectively. These findings contribute to the current understanding of psychopathology among adolescents following the pandemic and inform the development of mental health strategies for this particular population. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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