Department of Occupational Health, School of Health, Shahrekord University of Medical Sciences, Shahrekord, Iran.
Department of Occupational Health, School of Health, Kashan University of Medical Sciences, Kashan, Iran.
Front Public Health. 2024 Oct 8;12:1437112. doi: 10.3389/fpubh.2024.1437112. eCollection 2024.
Human error and the high rates of fatalities and other occupational accidents in the steel industry are of significant global relevance. The aim of this study was to investigate the effect of psychosocial, mental health, and burnout risk factors on human error probabilities in an industrial environment using Bayesian networks.
This cross-sectional study was conducted in 2023. The participants were 252 employees of a steel company. Error probabilities related to the tasks of participants were estimated using the Human Error Assessment and Reduction Technique (HEART). Other data was collected using a survey that consisted of demographic information, the Maslach Burnout Inventory, Depression Anxiety Stress Scales, and a short version of the Copenhagen Psychosocial Questionnaire. A theoretical model was drawn in GeNIe academic software (version 2.3).
The results showed that all the studied variables were able to significantly affect the distribution of human error probabilities. Considering a distribution of 100% for the high state of these variables, the results showed that the greatest increases in error probability were related to two burnout dimensions: emotional exhaustion (29%) and depersonalization (28%). All the variables, with a probability of 100%, increased the probability of high human error probabilities by 46%.
The most important variables in terms of their effect on human error probabilities were burnout dimensions, and these variables also had a mediation effect on the psychosocial and mental health variables. Therefore, preventive measures to control human error should first focus on managing the risks of burnout in workers. This, in turn, can also reduce the effect of psychosocial risk factors and mental health problems on human error in the workplace.
人为错误以及钢铁行业高死亡率和其他职业事故率是具有重大全球相关性的问题。本研究旨在使用贝叶斯网络调查工业环境中社会心理、心理健康和倦怠风险因素对人为错误概率的影响。
这是一项 2023 年进行的横断面研究。参与者为一家钢铁公司的 252 名员工。使用人为错误评估和降低技术（HEART）估计参与者相关任务的错误概率。使用问卷调查收集其他数据，该问卷包括人口统计学信息、马氏倦怠量表、抑郁焦虑压力量表和哥本哈根心理社会问卷的简短版本。在 GeNIe 学术软件（版本 2.3）中绘制理论模型。
结果表明，所有研究变量都能显著影响人为错误概率的分布。考虑到这些变量的高状态分布为 100%，结果表明，错误概率最大的增加与两个倦怠维度有关：情绪耗竭（29%）和去人性化（28%）。所有变量的概率为 100%时，会使高人为错误概率的概率增加 46%。
就其对人为错误概率的影响而言，最重要的变量是倦怠维度，这些变量对社会心理和心理健康变量也具有中介效应。因此，控制人为错误的预防措施应首先侧重于管理工人的倦怠风险。这反过来又可以降低工作场所社会心理风险因素和心理健康问题对人为错误的影响。
