基于贝叶斯网络的河北省雄安新区基层医疗卫生人员职业紧张及健康效应影响因素分析

Influencing factors of occupational stress and health effect among grassroots medical and health personnel in Xiong’an New Area, Hebei Province based on Bayesian network

  • 摘要:
    背景 基层医疗卫生人员是我国公共卫生体系的重要组成部分,其身心健康的保障将对我国健康事业的发展产生深远影响。
    目的 分析职业紧张、焦虑、抑郁和失眠的影响因素及因素间的相互作用。
    方法 于2021年8月,整群选取河北省雄安新区7家二级公立医院、8家卫生机构全体在职共2675人作为调查对象进行横断面调查。分别采用《付出-回报失衡量表》(ERI)、《广泛性焦虑量表》(GAD-7)、《病人健康问卷》(PHQ-9)、《阿森斯失眠量表》(AIS)对医疗卫生机构人员职业紧张、焦虑、抑郁、失眠情况进行评估。运用R4.2.1软件bnlearn、gmodels包构建工作、个人因素-职业紧张-健康效应贝叶斯网络。运用Netica32.0实现贝叶斯网络模型的可视化。
    结果 本次调查发放问卷3018份,收集有效问卷2675份,问卷有效回收率为88.63%。基层医疗卫生人员职业紧张、焦虑、抑郁、失眠阳性率分别为51.48%、62.13%、62.50%和56.37%。影响因素分析结果显示:不同年龄、文化程度、机构类型等的医疗卫生人员职业紧张阳性率差异有统计学意义(P<0.05),不同年龄、婚姻状况、日工作时长等的医疗卫生人员焦虑阳性率差异有统计学意义(P<0.05),不同性别、文化程度、内在投入情况等的医疗卫生人员抑郁、失眠阳性率差异有统计学意义(P<0.05)。构建的贝叶斯网络模型有14个节点、18条有向边,预测准确率为85.4%。职称、日工作时长、内在投入和锻炼直接影响职业紧张,其他工作、个人因素可通过间接作用影响职业紧张。职业紧张可直接影响失眠,也可通过影响焦虑间接影响失眠;焦虑、失眠均可影响抑郁的发生。
    结论 河北省雄安新区基层医疗卫生人员职业紧张、焦虑、抑郁、失眠程度较高。职业紧张会直接或间接影响焦虑、抑郁、失眠。工作、个人因素会影响职业紧张,也可通过职业紧张影响焦虑、抑郁、失眠。职业紧张是工作、个人因素影响焦虑、抑郁、失眠等心理健康问题的早期预警因素。要注意评估干预医疗卫生机构人员的职业紧张,以预防心理健康问题的发生。

     

    Abstract:
    Background Grassroots medical and health personnel are an important component of China's public health system, and guaranteeing their physical and mental health will have a profound impact on the development of China's health service.
    Objective To identify potential influencing factors of occupational stress, anxiety, depression, and insomnia as well as their interactions.
    Methods In August 2021, a cross-sectional survey was conducted among all the staff (2675 medical and health personnel) at 7 secondary public hospitals and 8 health institutions in Xiong’an New Area of Hebei Province by cluster sampling. Occupational stress, anxiety, depression, and insomnia were evaluated by the Effort-Reward Imbalance Questionnaire (ERI), the Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), and the Athens Insomnia Scale (AIS). The bnlearn and gmodels packages of R4.2.1 software were used to construct Bayesian networks on work and personal factors-occupational stress- health effects. The Bayesian network model was visualized by Netica32.0.
    Results Among the 3018 questionnaires distributed, a total of 2675 valid questionnaires were recovered, with an effective recovery rate of 88.63%. The positive rates of occupational stress, anxiety, depression, and insomnia were 51.48%, 62.13%, 62.50%, and 56.37%, respectively in the grassroots medical and health personnel. The positive rate of occupational stress among the medical and health workers varied by age, educational level, and organization type (P<0.05); the positive rate of anxiety varied by age, marital status, and daily working hours (P<0.05); and the positive rates of depression and insomnia varied by gender, educational level, and overcommitment (P<0.05). The final Bayesian network contained 14 nodes and 18 directed edges, and its prediction accuracy was 85.4%. .Job title, daily working hours, overcommitment and exercise directly associated with occupational stress, and other work and personal factors associated with occupational stress indirectly. Occupational stress associated with insomnia directly or indirectly via anxiety. Anxiety and insomnia associated with reporting depression.
    Conclusion Grassroots medical and health personnel of Xiong'an New Area, Hebei Province report high levels of occupational stress, anxiety, depression, and insomnia. Occupational stress directly or indirectly associates with anxiety, depression, and insomnia. Work and personal factors associate with occupational stress, and associate with anxiety, depression, and insomnia via occupational stress. Occupational stress may be an early warning factor of general work and personal factors affecting anxiety, depression, insomnia, and other mental health problems. Attention should be paid to the assessment and intervention of occupational stress in medical and health personnel to prevent mental health problems.

     

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