Caspian Journal of Internal Medicine
Babol University of Medical Sciences
Wed, Jan 15, 2025
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Volume 11 - Covid 19 Supplement 1
Caspian J Intern Med 2020, 11 - Covid 19 Supplement 1: 501-511 |
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Hashemi Amree M, Enayati T, Salehi M. Preventing the frequency of infectious diseases in vulnerable groups - by anticipating the role of actors in implementing the decision-making model in conditions of uncertainty pandemic experience Covid-19. Caspian J Intern Med 2020; 11 :501-511
URL: http://caspjim.com/article-1-2386-en.html
URL: http://caspjim.com/article-1-2386-en.html
Department of Educational Management, Sari Branch, Islamic Azad University, Sari, Iran , m.h.baran@gmail.com
Abstract: (3991 Views)
Background: The purpose of this study is to prevent the prevalence of infectious diseases in vulnerable groups by anticipating the role of actors in implementing decision-making models in conditions of uncertainty in medical universities.
Methods: This research is an applied research by combining qualitative and quantitative methods based on the foundation data theory (Grand Theory). To determine the dimensions of the model, the statistical population included crisis management managers and faculty members of Mazandaran University of Medical Sciences. The data collection has been done through targeted sampling and interviews, semi-structured interviews, analysis method, and coding method. The statistical population to present the model include senior and middle managers of Mazandaran University of Medical Sciences. The simple random sampling method based on the sample size was determined by Cochran's method, and the collected date from the researcher's questionnaire was analyzed through nonparametric statistical experiments, Kolmogorov test Smirnov, SPSS SMARTPLS, Excel and the method of modeling structural equations with the least squares approach has been partial.
Results: The path coefficient of each dimension in explaining the decision model in uncertainty conditions based on T statistic and p value and SRMR value was 0.137, which was a good value and the main actors in implementing the model were policymakers, managers and staff.
Conclusion: The implementation of this model will lead to a change in the decisions made by health system authorities in conditions of uncertainty, and will increase the ability of managers of medical universities and the resilience of the health system.
Methods: This research is an applied research by combining qualitative and quantitative methods based on the foundation data theory (Grand Theory). To determine the dimensions of the model, the statistical population included crisis management managers and faculty members of Mazandaran University of Medical Sciences. The data collection has been done through targeted sampling and interviews, semi-structured interviews, analysis method, and coding method. The statistical population to present the model include senior and middle managers of Mazandaran University of Medical Sciences. The simple random sampling method based on the sample size was determined by Cochran's method, and the collected date from the researcher's questionnaire was analyzed through nonparametric statistical experiments, Kolmogorov test Smirnov, SPSS SMARTPLS, Excel and the method of modeling structural equations with the least squares approach has been partial.
Results: The path coefficient of each dimension in explaining the decision model in uncertainty conditions based on T statistic and p value and SRMR value was 0.137, which was a good value and the main actors in implementing the model were policymakers, managers and staff.
Conclusion: The implementation of this model will lead to a change in the decisions made by health system authorities in conditions of uncertainty, and will increase the ability of managers of medical universities and the resilience of the health system.
Keywords: Covid-19, Decision making Model, Uncertainty, Grand Theory, University of Medical Sciences, vulnerable groups
Type of Study: Original Article |
Subject:
Health
Received: 2020/06/20 | Accepted: 2020/08/2 | Published: 2020/10/19
Received: 2020/06/20 | Accepted: 2020/08/2 | Published: 2020/10/19
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