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      A meta‐analysis on predictors of turnover intention of hospital nurses in South Korea (2000–2020)

      review-article
      1 , 2 ,
      Nursing Open
      John Wiley and Sons Inc.
      employee turnover, hospitals, meta‐analysis, nurses, predictors

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          Abstract

          Aims

          To identify the predictors of Registered Nurses’ turnover intention and analyse the effect sizes.

          Design

          A systematic review and meta‐analysis of previous research, conducted to comprehensively identify the predictors of turnover intention.

          Methods

          In total, 417 studies from 1 January 2000 to 30 April 2020 that investigated predictors of turnover intention of South Korean nurses were reviewed. The data were analysed using the R statistical package. Network graphs were used to analyse the relationships among turnover predictors, and meta‐analysis was performed to determine the effect sizes of the correlations.

          Results

          This review analysed common predictors identified in previous studies. Burnout (0.541), emotional exhaustion (0.511), job stress (0.390) and career plateau (0.386) showed positive effect sizes, while organizational commitment (−0.540), person−organizational fit (−0.521), career commitment (−0.508), work engagement (−0.503), job satisfaction (−0.491) and job embeddedness (−0.483) showed negative effect sizes.

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          Most cited references49

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

          David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses
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            JOB SATISFACTION, ORGANIZATIONAL COMMITMENT, TURNOVER INTENTION, AND TURNOVER: PATH ANALYSES BASED ON META-ANALYTIC FINDINGS

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              Power analysis for random-effects meta-analysis

              One of the reasons for the popularity of meta-analysis is the notion that these analyses will possess more power to detect effects than individual studies. This is inevitably the case under a fixed-effect model. However, the inclusion of the between-study variance in the random-effects model, and the need to estimate this parameter, can have unfortunate implications for this power. We develop methods for assessing the power of random-effects meta-analyses, and the average power of the individual studies that contribute to meta-analyses, so that these powers can be compared. In addition to deriving new analytical results and methods, we apply our methods to 1991 meta-analyses taken from the Cochrane Database of Systematic Reviews to retrospectively calculate their powers. We find that, in practice, 5 or more studies are needed to reasonably consistently achieve powers from random-effects meta-analyses that are greater than the studies that contribute to them. Not only is statistical inference under the random-effects model challenging when there are very few studies but also less worthwhile in such cases. The assumption that meta-analysis will result in an increase in power is challenged by our findings.
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                Author and article information

                Contributors
                kyung11@cbnu.ac.kr
                Journal
                Nurs Open
                Nurs Open
                10.1002/(ISSN)2054-1058
                NOP2
                Nursing Open
                John Wiley and Sons Inc. (Hoboken )
                2054-1058
                07 April 2021
                September 2021
                : 8
                : 5 ( doiID: 10.1002/nop2.v8.5 )
                : 2406-2418
                Affiliations
                [ 1 ] Seoul National University College of Nursing Seoul South Korea
                [ 2 ] Department of Nursing Chungbuk National University Cheongju South Korea
                Author notes
                [*] [* ] Correspondence

                Eun Gyung Kim, Chungbuk National University, Department of Nursing, Cheongju, South Korea.

                Email: kyung11@ 123456cbnu.ac.kr

                Author information
                https://orcid.org/0000-0002-0986-5217
                Article
                NOP2872
                10.1002/nop2.872
                8363357
                33826252
                7510eecd-98a7-4a8a-ae71-7df8a2d48ac3
                © 2021 The Authors. Nursing Open published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 17 December 2020
                : 07 October 2020
                : 29 January 2021
                Page count
                Figures: 2, Tables: 3, Pages: 13, Words: 8706
                Funding
                Funded by: Chungbuk National University , doi 10.13039/501100002461;
                Categories
                Review Article
                Review Articles
                Custom metadata
                2.0
                September 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.5 mode:remove_FC converted:13.08.2021

                employee turnover,hospitals,meta‐analysis,nurses,predictors

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