首页|火力发电职工职业健康心理因素对失眠影响的贝叶斯网络预测研究

火力发电职工职业健康心理因素对失眠影响的贝叶斯网络预测研究

Bayesian network prediction study on the impact of occupational health psychological factors on insomnia among thermal power generation workers

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目的 探讨火力发电行业职工失眠的风险因素及其相互作用的网络关系,为失眠高风险人群制定个性化干预提供科学依据.方法 于2022年11月,整群抽取某典型火力发电厂860名职工作为研究对象,采用职业卫生现场调查与问卷调查方法,收集基本信息、职业特征、焦虑、抑郁、压力、职业应激及失眠等相关信息.运用结构方程模型分析与贝叶斯网络构建,评估失眠与职业健康心理因素相互作用关系.结果 860名火力发电职工焦虑、抑郁和压力症状检出率分别为34.0%(292/860)、32.1%(276/860)和18.0%(155/860),职业应激总分为(445.3±49.9)分,职工可疑失眠 160 人(18.6%),失眠 578 人(67.2%).结构方程模型分析显示,职业应激对火力发电职工失眠的发生存在明显效应(标准化载荷系数为0.644),职业健康心理对失眠的影响较低(标准化载荷系数为0.065).但贝叶斯网络模型进一步分析发现,焦虑和压力是失眠的2个父节点,为直接因果关系,弧线强度分别为-8.607、-15.665;模型预测结果显示,在无压力与焦虑、低压力无焦虑、无压力低焦虑时,失眠发生的概率预测均为0;当出现高压力低焦虑和低压力高焦虑时,失眠发生的概率预测分别为0.38、0.47;当高压力高焦虑同时发生时,失眠发生的概率预测为0.51.结论 贝叶斯网络风险评估可直观揭示并预测火力发电职工失眠风险及风险间的网络作用关系,焦虑和压力是失眠发生的直接因果风险,压力是火力发电职工个体失眠发生的主要风险,可基于压力状况的科学干预来降低失眠的发生.
Objective To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions,and to provide scientific basis for personalized interventions for high-risk groups with insomnia.Methods In November 2022,860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling.On-site occupational health field surveys and questionnaire surveys were used to collect basic information,occupational characteristics,anxiety,depression,stress,occupational stress,and insomnia.The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction.Results The detection rates of anxiety,depression and stress were 34.0%(292/860),32.1%(276/860)and 18.0%(155/860),respectively.The total score of occupational stress was(445.3±49.9)points,and 160 workers(18.6%)were suspected of insomnia,and 578 workers(67.2%)had insomnia.Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers(standardized load coefficient was 0.644),and occupational health psychology had a low effect on insomnia(standardized load coefficient was 0.065).However,the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia,with direct causal relationships,the arc strength was-8.607 and-15.665,respectively.The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety,low stress without anxiety,and no stress with low anxiety.When high stress with low anxiety and low stress with high anxiety occurred,the predicted probability of insomnia occurring were 0.38 and 0.47,respectively.When both high stress and high anxiety occurred simultaneously,the predicted probability of insomnia occurring was 0.51.Conclusion Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks.Anxiety and stress are the direct causal risks of insomnia,and stress is the main risk of individual insomnia of thermal power generation workers.The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

InsomniaSleep disordersOccupational health risksBayesian networksThermal power generation

崔方方、盛佩佳、马景璇、石婷、王永伟

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四川大学华西公共卫生学院(华西第四医院)职业危害评价科,四川省职业卫生应急(甲级)重点实验室,四川大学华西-协和陈志潜卫生健康研究院卫生应急管理研究中心,成都 610041

新疆昌吉州妇幼保健院保健部,昌吉 831100

四川大学华西公共卫生学院(华西第四医院)劳动卫生与环境卫生学系,成都 610041

失眠症 睡眠障碍 职业健康风险 贝叶斯网络 火力发电

阿坝州应用技术研究与开发资金项目

R23YYJSYJ0019

2024

中华劳动卫生职业病杂志
中华医学会

中华劳动卫生职业病杂志

CSTPCD北大核心
影响因子:0.787
ISSN:1001-9391
年,卷(期):2024.42(6)