中国安全科学学报 ›› 2026, Vol. 36 ›› Issue (2): 235-243.doi: 10.16265/j.cnki.issn1003-3033.2026.02.0322

• 职业健康 • 上一篇    下一篇

基于贝叶斯网络的男职工噪声暴露与负性情绪路径分析

段昕昀1(), 李林玥1, 马景璇1, 王永伟1,2,3,**()   

  1. 1 四川大学 华西公共卫生学院(华西第四医院),四川 成都 610041
    2 四川省职业卫生应急(甲级)重点实验室,四川 成都 610041
    3 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,四川 成都 610041
  • 收稿日期:2025-09-16 修回日期:2025-11-20 出版日期:2026-02-28
  • 通信作者:
    ** 王永伟(1980—,男,河南周口人,博士,主任医师,主要从事职业中毒和职业流行病学研究。E-mail:
  • 作者简介:

    段昕昀 (1999—),男,四川广安人,硕士研究生,研究方向为职业中毒与职业流行病学。E-mail:

  • 基金资助:
    四川省阿坝藏族羌族自治州科技重点项目(R23YYJSYJ0019)

Path analysis of negative emotions in high-noise-exposed male workers based on Bayesian network models

DUAN Xinyun1(), LI Linyue1, MA Jingxuan1, WANG Yongwei1,2,3,**()   

  1. 1 West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu Sichuan 610041, China
    2 Key Laboratory of Occupational Safety and Health, Sichuan Province, Chengdu Sichuan 610041, China
    3 Health Emergency Management Research Center, China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu Sichuan 610041, China
  • Received:2025-09-16 Revised:2025-11-20 Published:2026-02-28

摘要:

为探究职业噪声暴露人员负性情绪的影响路径,首先以5家典型制造业中493名高噪声暴露男职工作为研究对象,通过职业卫生调查、噪声测定及心理量表收集数据;然后采用最小绝对收缩和选择算子(LASSO)回归筛选出年龄、累积噪声暴露(CNE)、婚姻状况等9个关键变量,并据此构建贝叶斯网络模型。结果显示,男职工负性情绪检出率为5.7%,平均噪声暴露水平为91.5 dB(A);模型识别出多条影响路径,其概率分布存在年龄差异:<30岁组以“年龄→婚姻状况→负性情绪”为主(16.4%),30~39岁组以“年龄→负性情绪”为主(30.6%),而≥40岁组则以“年龄→CNE→负性情绪”为主(21.5%~29.4%),且高CNE组负性情绪发生概率普遍高于中暴露组。研究表明:高噪声暴露下男职工的负性情绪产生受年龄、CNE、吸烟、饮酒及婚姻状况等因素交互影响,贝叶斯网络能有效揭示其复杂路径关系。

关键词: 贝叶斯网络, 男职工, 高噪声暴露, 负性情绪, 路径分析

Abstract:

In order to explore the influencing pathways of negative emotions among occupational noise-exposed workers, 493 male workers with high noise exposure from five typical manufacturing enterprises were recruited as subjects. Data were collected through occupational health surveys, noise measurements, and psychological scales. Key variables, including age, cumulative noise exposure (CNE), marital status, and nine other factors, were screened using the least absolute shrinkage and selection operator (LASSO) regression, based on which a Bayesian network model was constructed. The results showed that the detection rate of negative emotions among male workers was 5.7%, with an average noise exposure level of 91.5 dB(A). The model identified multiple influencing pathways, and their probability distributions varied across age groups: in the <30 years group, the pathway “age → marital status → negative emotions” was predominant (16.4%); in the 30-39 years group, the direct pathway “age → negative emotions” was most prominent (30.6%); while in the ≥40 years group, the pathway “age → CNE → negative emotions” was dominant (21.5%-29.4%). Moreover, the high CNE group generally exhibited a higher probability of negative emotions than the medium exposure group. The study indicates that negative emotions among male workers under high noise exposure are interactively influenced by factors such as age, CNE, smoking, drinking, and marital status. The Bayesian network model effectively reveals these complex pathway relationships.

Key words: Bayesian network, male workers, high noise exposure, negative emotions, path analysis

中图分类号: