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      The Interactive Effect of COVID-19 Risk and Hospital Measures on Turnover Intentions of Healthcare Workers: A Time-Lagged Study

      , ,
      International Journal of Environmental Research and Public Health
      MDPI AG

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          Abstract

          COVID-19 has led to a global health emergency worldwide. As a result, healthcare workers undergo distress mainly due to the perceived risk of contracting the virus. Such stress might cause them to leave their jobs. In this context, the current study: (1) introduced the concept of perceived risk of COVID-19 and measured it by adapting and validating an existing scale available on the risk of infectious diseases and (2) investigated its outcomes, underlying mechanisms, and boundary conditions for healthcare workers. With the support of conservation of resources theory, the current study aimed to investigate the association between perceived risk of COVID-19 and turnover intentions among healthcare workers, particularly Doctors, nurses, and paramedics staff. This study also aimed to investigate the mediating role of perceived fear of COVID-19 between perceived risk of COVID-19 and turnover intention. The current study also aimed to examine the buffering role that perceptions of hospital measures against COVID-19 could have on diminishing workers’ turnover intentions. Data were collected through a three time-lag email survey of healthcare workers in Pakistan (N = 178) who currently provide treatment to COVID-19 patients. The results supported the hypothesis that perceived risk of COVID-19 enhances fear of COVID-19 among healthcare workers and, consequently, their turnover intentions. Perceptions of hospital measures against COVID-19 weaken the relationship between perceived risk of COVID-19 and fear of COVID-19, which reduces turnover intentions of health care workers. The current study offers implications for theory, practitioners, and society.

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          Most cited references60

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          Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China

          In December 2019, novel coronavirus (2019-nCoV)-infected pneumonia (NCIP) occurred in Wuhan, China. The number of cases has increased rapidly but information on the clinical characteristics of affected patients is limited.
            Bookmark
            • Record: found
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            • Article: not found

            Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

            G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A power primer.

              One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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                Author and article information

                Journal
                IJERGQ
                International Journal of Environmental Research and Public Health
                IJERPH
                MDPI AG
                1660-4601
                October 2021
                October 12 2021
                : 18
                : 20
                : 10705
                Article
                10.3390/ijerph182010705
                34682450
                de03ea66-6668-430d-95d1-b322d45cb330
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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