The interrelations between psychological outcome trajectories and resource changes amid large-scale disasters: A growth mixture modeling analysis

Translational psychiatry(2023)

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摘要
Currently little is known about the interrelations between changes in psychiatric symptoms and changes in resources (personal, social, financial) amid large-scale disasters. This study investigated trajectories of psychiatric symptoms and their relationships with different patterns of changes in personal, social, and financial resources between 2020 and 2022 amid the COVID-19 pandemic. A population-representative sample ( N = 1333) was recruited to complete self-report instruments at the pandemic’s acute phase (February–July 2020, T1), and again at 1-year (March–August 2021, T2) and 1.5-year (September 2021–February 2022, T3) follow-ups. Respondents reported depressive and anxiety symptoms, self-efficacy, perceived social support, and financial capacity. Growth mixture modeling (GMM) identified four trajectories of depressive and anxiety symptoms: resilience (72.39–74.19%), recovery (8.40–11.93%), delayed distress (7.20–7.35%), and chronic distress (8.33–10.20%). Four patterns were demonstrated in resource changes: persistent high resources (40.89–47.64%), resource gain (12.08–15.60%), resource loss (6.30–10.43%), and persistent low resources (28.73–36.61%). Loss and gain in financial resources characterized chronic distress and resilience , respectively. Loss in personal resources characterized delayed distress , whereas loss or no gain in social resources was related to chronic/delayed distress . Respondents in resilience were also more likely to have persistent high resources while those with delayed/chronic distress were more likely to have persistent low resources. These results provide an initial evidence base for advancing current understanding on trajectories of resilience and psychopathology in the context of resource changes during and after large-scale disasters.
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关键词
psychological outcome trajectories,growth mixture modeling analysis,resource changes,large-scale
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