Iraj Mohammadfam1, Fakhradin Ghasemi2, Omid Kalatpour1, Abbas Moghimbeigi3. 1. Center of Excellence for Occupational Health, Research Center for Health Science, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. 2. Center of Excellence for Occupational Health, Research Center for Health Science, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran. Electronic address: f.ghasemi@umsha.ac.ir. 3. Department of Biostatistics and Epidemiology, School of Public Health and Modeling of Non-communicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
Abstract
INTRODUCTION: Unsafe behavior increases the risk of accident at workplaces and needs to be managed properly. The aim of the present study was to provide a model for managing and improving safety behavior of employees using the Bayesian networks approach. METHODS: The study was conducted in several power plant construction projects in Iran. The data were collected using a questionnaire composed of nine factors, including management commitment, supporting environment, safety management system, employees' participation, safety knowledge, safety attitude, motivation, resource allocation, and work pressure. In order for measuring the score of each factor assigned by a responder, a measurement model was constructed for each of them. The Bayesian network was constructed using experts' opinions and Dempster-Shafer theory. Using belief updating, the best intervention strategies for improving safety behavior also were selected. RESULTS: The result of the present study demonstrated that the majority of employees do not tend to consider safety rules, regulation, procedures and norms in their behavior at the workplace. Safety attitude, safety knowledge, and supporting environment were the best predictor of safety behavior. Moreover, it was determined that instantaneous improvement of supporting environment and employee participation is the best strategy to reach a high proportion of safety behavior at the workplace. CONCLUSION: The lack of a comprehensive model that can be used for explaining safety behavior was one of the most problematic issues of the study. Furthermore, it can be concluded that belief updating is a unique feature of Bayesian networks that is very useful in comparing various intervention strategies and selecting the best one form them.
INTRODUCTION: Unsafe behavior increases the risk of accident at workplaces and needs to be managed properly. The aim of the present study was to provide a model for managing and improving safety behavior of employees using the Bayesian networks approach. METHODS: The study was conducted in several power plant construction projects in Iran. The data were collected using a questionnaire composed of nine factors, including management commitment, supporting environment, safety management system, employees' participation, safety knowledge, safety attitude, motivation, resource allocation, and work pressure. In order for measuring the score of each factor assigned by a responder, a measurement model was constructed for each of them. The Bayesian network was constructed using experts' opinions and Dempster-Shafer theory. Using belief updating, the best intervention strategies for improving safety behavior also were selected. RESULTS: The result of the present study demonstrated that the majority of employees do not tend to consider safety rules, regulation, procedures and norms in their behavior at the workplace. Safety attitude, safety knowledge, and supporting environment were the best predictor of safety behavior. Moreover, it was determined that instantaneous improvement of supporting environment and employee participation is the best strategy to reach a high proportion of safety behavior at the workplace. CONCLUSION: The lack of a comprehensive model that can be used for explaining safety behavior was one of the most problematic issues of the study. Furthermore, it can be concluded that belief updating is a unique feature of Bayesian networks that is very useful in comparing various intervention strategies and selecting the best one form them.
Authors: Albert P C Chan; Francis K W Wong; Carol K H Hon; Tracy N Y Choi Journal: Int J Environ Res Public Health Date: 2018-11-08 Impact factor: 3.390
Authors: Guodong Ni; Yuanyuan Zhu; Ziyao Zhang; Yaning Qiao; Huaikun Li; Na Xu; Yongliang Deng; Zhenmin Yuan; Wenshun Wang Journal: Int J Environ Res Public Health Date: 2020-11-12 Impact factor: 3.390