职业紧张和社会支持对某国网公司员工抑郁症状的影响

Effects of occupational stress and social support on depressive symptoms among employees from a state grid corporation

[目的]了解国网公司员工职业紧张、社会支持和抑郁症状现况,分析职业紧张和社会支持对抑郁症状的影响。[方法]于2017年3月,采用横断面研究设计,通过整群抽样方法选择国网某区公司1 069名在编员工为调查对象。经知情同意后,由调查对象自主填写完成问卷。采用简明职业紧张问卷和病人健康问卷(PHQ-9)对调查对象的职业紧张、社会支持和抑郁症状进行评估。采用t检验和ANOVA分析比较职业紧张及抑郁症状在不同个体特征组间的差异。采用二分类logistic回归模型分析职业紧张和社会支持与抑郁症状的关联。[结果]本研究回收问卷有效率为87.1%。调查对象职业紧张平均得分为1.15±0.33,职业紧张阳性率为63.1%,不同年龄、受教育程度、周工作时长和工作制组间职业紧张得分存在差异(P <0.01);抑郁症状阳性率为66.7%,不同年龄、家庭人均月收入和周工作时长组间抑郁症状得分和抑郁症状阳性率存在差异(P <0.01);logistic回归分析结果显示,高工作要求(OR=1.97)是抑郁症状的危险因素,高工作自主性(OR=0.73)和高社会支持(OR=0.53)是抑郁症状的保护因素。[结论]高工作要求,低工作自主性,低社会支持可能增加电网公司员工抑郁症状的发生风险。

[ Objective ] To investigate the status of occupational stress, social support, and depressive symptoms of employees in a state grid corporation, and analyze the effects of occupational stress and social support on depressive symptoms. [ Methods ] In March 2017, a total of 1069 workers from a state grid corporation were enrolled in a cross-sectional survey through cluster sampling method, and were asked to complete a self-administered questionnaire after informed consent. Brief Occupational Stress Questionnaire and Patient Health Questionnaire （PHQ-9） were used to assess occupational stress, social support, and depressive symptoms. The t-test and ANOVA were used to compare the differences in occupational stress and depressive symptoms among subjects with different characteristics. Binary logistic regression model was used to assess the associations of depressive symptoms with occupational stress and social support. [ Results ] The valid response rate of questionnaires reached 87.1% in this study. The average occupational stress score of the respondents was 1.15±0.33, the positive rate of occupational stress was 63.1%, and there were significant differences in occupational stress scores among groups with different ages, educational levels, weekly working hours, and shift modes （P〈 0.01）. The positive rate of depressive symptoms reached 66.7%, and there were significant differences in the scores of PHQ-9 and the positive rates of depl-ssive symptoms among groups with different ages, monthly household income per capita, and weekly working hours （P〈0.01）. The results of logistic regression analyses showed that high job demand （OR=1.97） was a risk factor of depressive symptoms, while high for control （OR=0.73） and social support （OR=0.53） were protective factors. [ Conclusion ] High job demand, low joh control, and low social support may increase the risk of depressive symptoms among employees from the selected state grid corporation.