文章基本信息

标题：Psychosocial Factors and Chronic Illness as Predictors for Anxiety and Depression in Adolescence
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作者：Lacomba-Trejo, Laura ; Valero-Moreno, Selene ; Montoya-Castilla, Inmaculada 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2020
卷号：11
页码：1-9
DOI：10.3389/fpsyg.2020.568941
出版社：Frontiers Media
摘要：Adolescence is a challenging time when emotional difficulties often arise. Self-esteem, good relationships with peers, and emotional competences can buffer the effects of these difficulties. The difficulties can be even greater when coupled with the presence of a chronic physical illness (CD). Our goal is to analyze psychosocial factors and CD as predictors for anxiety and depression. It was compared the results of structural equation models (SEM) with models based on qualitative comparative analysis (QCA) to analyze the possible influence of these variables on levels of anxiety-depression in adolescents with and without CD. The sample consisted of 681 adolescents, between 12-16 years old (M=13.94, SD=1.32). 61.50% were girls and 13.40% (n= 222) presented a CD (mainly pneumo-allergic and endocrine). They were evaluated by the Hospital Anxiety and Depression Scale, the Self-esteem Questionnaire, the Emotional Competences Questionnaire and the Strengths and Difficulties Questionnaire. The results obtained by SEM show that low self-esteem, problems with peers and low emotional competencies predict anxiety in 41% of the variance and depression in 72%. The results obtained by QCA show that the different combinations of these variables explain between 24-61% of low levels of anxiety and depression and 47-55% of high levels. Our data show how the presence of a CD, low self-esteem, problems with peers and problems in emotional skills play a fundamental role in explaining levels of anxiety and depression. These aspects will help provide increased resources for emotional adjustment in the educational context, facilitating the transitions to be made by adolescents.
关键词：adolescent; chronic illness; qualitative comparative analysis; structural equation models; adjustment