Isaac L. Ahuvia, Sharon Chen, Lucy H. Gordon, Kathryn R. Fox, J. Schleider
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引用次数: 0
Abstract
The goal of the present study was to better understand adolescents’ beliefs about what causes depression and how these beliefs relate to other clinical constructs. This study explored the causal beliefs about depression held by a diverse sample of U.S. adolescents with elevated depression symptoms ( N = 281; 55% White non-Hispanic; 53% cisgender girl; 78% LGBQ*). Qualitative methods were used to identify causal beliefs from open-ended survey responses. Quantitative methods compared the perceived causes of one’s own depression versus others’ depression, compared causal beliefs across groups, and measured the association between causal beliefs and additional clinical constructs. The most common causal beliefs were dysfunctional home and family relationships (52%) and stress from school (42%). Several causal beliefs were expressed more in regards to one’s own depression than others’ depression (e.g., adverse childhood events, 11% vs. 3%, p = .004) and vice versa (e.g., social media use, 12% vs. 2%, p < .001). Few significant relationships emerged between causal beliefs and demographic and clinical variables. Adolescents’ causal beliefs about depression are diverse and multifaceted, and their causal beliefs endorsed about their own depression differ substantially from those endorsed about depression generally. However, associations between adolescents’ causal beliefs and clinical variables appear limited.
期刊介绍:
The aim of the Journal of Adolescent Research is to publish lively, creative, and informative articles on development during adolescence (ages 10-18) and emerging adulthood (ages 18-25). The journal encourages papers that use qualitative, ethnographic, or other methods that present the voices of adolescents. Few strictly quantitative, questionnaire-based articles are published in the Journal of Adolescent Research, unless they break new ground in a previously understudied area. However, papers that combine qualitative and quantitative data are especially welcome.