安徽地区大学生抑郁症危险因素的二元logistic回归分析
Binary logistic regression analysis of risk factors of undergraduate depression in Anhui district
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摘要: 目的:探讨大学生抑郁症的危险因素,为高校健全大学生心理预警机制和教育提供依据。方法:随机抽取安徽省内6所大学2 593名学生,统一填写一般情况调查表(包括年龄、年级、家庭和谐程度、专业满意度等)和一份标准化的抑郁自评量表。采用二元logistic回归分析进行危险因素筛选。结果:27.70%的学生抑郁症状明显,其中轻度抑郁19.21%,中度抑郁7.52%,重度抑郁1.00%,抑郁严重度为0.480.10。性别、家庭经济状况、家庭和睦程度、情感状态、专业满意度、体育锻炼、社会活动和人际关系与抑郁症状的产生均有一定关系(P0.05~P0.01),年级、独生子女、单亲家庭和儿童留守情况与抑郁症均无明显关系(P0.05)。结论:多种因素参与大学生抑郁症的发生过程,其危险因素应受到更多的重视。建立有效的心理筛查与预警机制、及时的跟踪和干预,是防止大学生心理健康恶化的根本。
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关键词:
- 抑郁症 /
- 大学生 /
- 危险因素 /
- 二元logistic回归分析
Abstract: Objective:To explore the risk factors of undergraduate depression and provide the basis for developing psychological warning mechanism and education.Methods:Two thousand five hundred and ninety-three undergraduates from six universities were randomly selected and required to fill out an uniform general questionnaire(including some basic information such as age,grade,family harmony degree,profession satisfaction,etc.) and the standardized Self-rating Depression Scale.The risk factors were analysed by binary logistic regression procedure.Results:The depression symptoms of 27.70% undergraduates were evident,which included 19.21% mild,7.52% moderate and 1.00% severe depressive symptoms.The mean depression severity was 0.480.10.There were a certain relation between gender,family financial situation,family harmony degree,affectional status,profession satisfaction,physical exercise,social activity and interpersonal relationship and depressive symptoms(P0.05 to P0.01).The grade,only-child,single-parent family and stay-at-home children were not significantly associated with depression(P0.05).Conclusions:A variety of factors are involved in the occurrence of undergraduate depression.Paying attention to the risk factors,establishing effective psychological screening and early warning mechanisms and timely tracking and intervention is the foundation of preventing the mental health deteriorating of undergraduate.-
Key words:
- depression /
- undergraduate /
- risk factors /
- binary logistic regression analysis
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