Volume 11, Issue 3 (9-2021)
J Health Saf Work 2021, 11(3): 516-528 |
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Allahyari E, Gholami A, Arab-Zozani M, Ameri H, Nasseh N. Using emotional intelligence to predict job stress: Artificial neural network and regression models. J Health Saf Work 2021; 11 (3) :516-528
URL: http://jhsw.tums.ac.ir/article-1-6543-en.html
URL: http://jhsw.tums.ac.ir/article-1-6543-en.html
1- Department of Epidemiology and Biostatistics, School of Health, Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
2- Department of Occupational Health, School of Health Social Determinants of Health Research Center Birjand University of Medical Sciences, Birjand, Iran
3- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
4- Health Policy and Management Research Center, Department of Health Services Management, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
5- Social Determinants of Health Research Center, Faculty of Health, Environmental Health Engineering Department, Birjand University of Medical Sciences, Birjand, Iran ,negin_nassseh@yahoo.com
2- Department of Occupational Health, School of Health Social Determinants of Health Research Center Birjand University of Medical Sciences, Birjand, Iran
3- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
4- Health Policy and Management Research Center, Department of Health Services Management, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
5- Social Determinants of Health Research Center, Faculty of Health, Environmental Health Engineering Department, Birjand University of Medical Sciences, Birjand, Iran ,
Abstract: (1834 Views)
Introduction: These days, there is a consensus that emotional intelligence plays an important role in the success of individuals in different areas of life. Persons with higher emotional intelligence had lower stress in dealing with demands and pressures in the workplace. The purpose of this study was to use artificial neural network to predict job stress and to compare the performance of this model with the multivariate regression model.
Material and Methods: In order to do that, 892 participants were selected randomly in different job categories. Then, 15 dimensions of Bar-On questionnaire, 10 job categories, age and education were considered as input variables and 7 dimensions of health and safety executive HSE were determined as output variables in models.
Results: The results revealed that an artificial neural network with hyperbolic tangent and sigmoid transfer functions respectively in hidden and output layers with 375 hidden neurons had significantly better performance than multivariate regression. So that, correlation of predicted values and job stress were only between 0.192-0.364 in regression model, but neural network had at least correlation 0.527 in all dimensions of job stress.
Conclusion: In predicting job stress using emotional intelligence, artificial neural network method was much better than multivariate regression model.
Material and Methods: In order to do that, 892 participants were selected randomly in different job categories. Then, 15 dimensions of Bar-On questionnaire, 10 job categories, age and education were considered as input variables and 7 dimensions of health and safety executive HSE were determined as output variables in models.
Results: The results revealed that an artificial neural network with hyperbolic tangent and sigmoid transfer functions respectively in hidden and output layers with 375 hidden neurons had significantly better performance than multivariate regression. So that, correlation of predicted values and job stress were only between 0.192-0.364 in regression model, but neural network had at least correlation 0.527 in all dimensions of job stress.
Conclusion: In predicting job stress using emotional intelligence, artificial neural network method was much better than multivariate regression model.
Type of Study: Research |
Received: 2021/09/20 | Accepted: 2021/09/1 | Published: 2021/09/1
Received: 2021/09/20 | Accepted: 2021/09/1 | Published: 2021/09/1
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