Sensitivity analysis of human error in the steel industry

Exploring the effects of psychosocial and mental health risk factors and burnout using Bayesian networks

Bibliographic Data

ID22070646
AuthorsSaeid Yazdanirad (0000-0002-5251-6637, Shahrekord University of Medical Sciences), Amir Hossein Khoshakhlagh (0000-0002-2265-5054, Kashan University of Medical Sciences, corresponding author), Saleh Al Sulaie (0000-0002-2530-8074, Umm al-Qura University), Rosanna Cousins (0000-0003-4829-5138, Digital Science (United Kingdom)), Mohammad Dehghani (0000-0001-8051-5976), Mohammad Reza Dehghani (0009-0005-7083-0135, Kashan University of Medical Sciences), Reza Khodakhah (Kashan University of Medical Sciences), Saeid Shabanitabar (University of Kashan)
Year2024
Volume12
Pages1437112-1437112
Publication date2024-10-08
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2024.1437112
PMID39440175
OpenAlexW4403236829
LanguageEN
References cited54

Introduction: 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. Methods: 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). Results: 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%. Conclusion: 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

Bayesian network · Burnout · Environmental health · Machine learning · Mental health · Psychiatry · Psychosocial · Applied Psychology · Clinical Psychology · Computer Science · Engineering · Medicine · Occupational Health and Safety Research · Patient Safety and Medication Errors · Psychology · Risk and Safety Analysis

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