Volume 10, Issue 4 (Occupational Medicine Quarterly Journal 2019)
tkj 2019, 10(4): 62-73 |
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Ethics code: IR.SSU. REC.1398.123
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Shekari H. Forecasting Job Burnout among University Faculty Members of Yazd Payame Noor University Using Artificial Neural Network Technique. tkj 2019; 10 (4) :62-73
URL: http://tkj.ssu.ac.ir/article-1-983-en.html
URL: http://tkj.ssu.ac.ir/article-1-983-en.html
Payame Noor University , h.shekari@ymail.com
Abstract: (2532 Views)
Background: Faculty members are one of the main factors in the higher education system, that high level of occupational stress caused by educational, research, and executive duties makes them exposed to burnout. The purpose of this study is Forecasting burnout of faculty members of Yazd Payame Noor University using artificial neural network technique.
Methods: The present research is descriptive in terms of method, and applied in terms of purpose. The statistical population of this research is the faculty members of Yazd Payame Noor University. The analysis was performed on 315 data from 105 faculty members that were acquired during the last three academic years. Data were collected using two closed questionnaires. Data were analyzed using SPSS software version 22. For analysis of data including 23 independent variables and one dependent variable, two types of neural network including MLP and RBF were designed and implemented.
Results: Correct percent of burnout prediction in the training, testing and validation data for the MLP neural network was 83.3, 80.9 and 74.5, respectively, and 73.1, 93.3 and 9/76 for the RBF neural network, respectively. The area under the rock for MLP and RBF networks was 0.823 and 0.833 respectively.
Conclusion: Comparison of two MLP and RBF neural networks based on rock curve and prediction Correct percent showed that RBF neural network is more effective in forecasting job burnout of faculty members of Yazd Payame Noor University, and the variables scientific group, teaching master students, age and communication had the greatest impact on burnout.
Methods: The present research is descriptive in terms of method, and applied in terms of purpose. The statistical population of this research is the faculty members of Yazd Payame Noor University. The analysis was performed on 315 data from 105 faculty members that were acquired during the last three academic years. Data were collected using two closed questionnaires. Data were analyzed using SPSS software version 22. For analysis of data including 23 independent variables and one dependent variable, two types of neural network including MLP and RBF were designed and implemented.
Results: Correct percent of burnout prediction in the training, testing and validation data for the MLP neural network was 83.3, 80.9 and 74.5, respectively, and 73.1, 93.3 and 9/76 for the RBF neural network, respectively. The area under the rock for MLP and RBF networks was 0.823 and 0.833 respectively.
Conclusion: Comparison of two MLP and RBF neural networks based on rock curve and prediction Correct percent showed that RBF neural network is more effective in forecasting job burnout of faculty members of Yazd Payame Noor University, and the variables scientific group, teaching master students, age and communication had the greatest impact on burnout.
Type of Study: Research |
Subject:
occupational medicine
Received: 2018/11/18 | Accepted: 2019/11/4 | Published: 2019/11/4
Received: 2018/11/18 | Accepted: 2019/11/4 | Published: 2019/11/4
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