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      Factors affecting shift work sleep disorder in female shift work nurses: Integrating shift workers’ coping with stress and transactional stress coping theory

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          Abstract

          Background:

          Nurses have to work different and often irregular shifts to provide care. This puts nurses at health risks, such as sleep impairment.

          Design and methods:

          The aim of this study was to test the comprehensive conceptual framework for predicting shift work sleep disorder of female nurses, we used a structural equation model analysis based on shift worker’s coping and transactional stress coping theory. This study used a cross-sectional design. We collected the data from three public hospitals and three private hospitals in South Kalimantan, Indonesia, with a total sample of 201 female shift work nurses. Data were collected from February to April 2020. We were also granted permission by the director and the head nurse of these hospitals. After obtaining the informed consent forms, we distributed the online self-report questionnaire using Google Forms. Demographic data were evaluated using descriptive statistical analysis. We used a structural equation model analysis to test the comprehensive conceptual framework for predicting shift work sleep disorder of female shift work nurses.

          Results:

          The model’s effectiveness in predicting factors contributing to shift work sleep disorder was confirmed by the good statistical fit indicated by the comparative fit index, root-mean-square error of approximation, incremental fit index, and Tucker Lewis Index.

          Conclusions:

          This study provides evidences that workload and interpersonal conflict contribute to occupational stress. Workload, interpersonal conflict, and the biological sleep clock influences shift work sleep disorder through mediators of coping strategies and stress.

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          Most cited references38

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          Starting at the beginning: an introduction to coefficient alpha and internal consistency.

          Cronbach's a is the most widely used index of the reliability of a scale. However, its use and interpretation can be subject to a number of errors. This article discusses the historical development of a from other indexes of internal consistency (split-half reliability and Kuder-Richardson 20) and discusses four myths associated with a: (a) that it is a fixed property of the scale, (b) that it measures only the internal consistency of the scale, (c) that higher values are always preferred over lower ones, and (d) that it is restricted to the range of 0 to 1. It provides some recommendations for acceptable values of a in different situations.
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            Using Multivariate Statistics

            After the Introduction Chapter, the second Chapter gives a guide to the multivariate techniques that are covered in this book and palces them in context with the more familiare univeriate and bivariate statistics where possible. Included in this chapter is a flow chart that organizes statistical techniques on the basis of the major research questions asked. Chapter three provides a brief review of univariate and bivariate statistical techniques for those who are interested. Chapter four deals with the assumptions an limitations of mulitvariate statistical methods. Assessment and violation of assumptions are discussed, along with alternatives for dealing with violations when they occur. This chapter is also meant to be referred to often, and the reader ist guided back to it frequently in Chapters five through sixteen an eighteen (online). Chapters five through sixteen and eighteen (online) cover specific multivariate techniques. They include descriptive, conceptual sections as well as a guided tour through a real-world data set for which the analysis is apporopriate. The tour includes an example of a Results section describing the outcome of the statistical analysis apporopriate for submissions to a professional journal. Each technique chapter includes a comparision of cumputer programs. Chapter seventeen is an attempt to integrate univariate, bivariate, and multivariate statistics through the multivariate general linear model. The common elements underlying all the techniques are emphasized, rather than the differences among them. This Chapter is ment to pull together the material in the remainder of the book with a conceptual rather than pragmatic emphasis.
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              Principled missing data methods for researchers

              The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
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                Author and article information

                Journal
                J Public Health Res
                J Public Health Res
                PHJ
                spphj
                Journal of Public Health Research
                SAGE Publications (Sage UK: London, England )
                2279-9028
                2279-9036
                24 June 2023
                April 2023
                : 12
                : 2
                : 22799036231182026
                Affiliations
                [1 ]Public Health Faculty, Universitas Airlangga, Surabaya, Indonesia
                [2 ]School of Nursing, Faculty of Medicine, Universitas Lambung Mangkurat, Banjarbaru, Indonesia
                [3 ]Department of Community and Mental Health Nursing, Faculty of Nursing, Universitas Airlangga, Surabaya, Indonesia
                [4 ]Department of Maternal and Child Health Nursing, Faculty of Nursing, Universitas Airlangga, Surabaya, Indonesia
                [5 ]Nursing Department, Poltekkes Kemenkes, Banjarmasin, Indonesia
                Author notes
                [*]Anggi Setyowati, School of Nursing, Faculty of Medicine, Universitas Lambung Mangkurat, Jalan Ahmad Yani KM 36, Banjarbaru, Kalimantan Selatan 70714, Indonesia. Email: anggisetyo@ 123456ulm.ac.id
                Author information
                https://orcid.org/0000-0002-0999-895X
                Article
                10.1177_22799036231182026
                10.1177/22799036231182026
                10291410
                37378002
                0dfed49e-41e9-477e-8e50-174992f2459e
                © The Author(s) 2023

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 2 January 2023
                : 1 May 2023
                Funding
                Funded by: Ministry of Research and Technology / National Research and Innovation Agency Indonesia, 2021 and Universitas Lambung Mangkurat, Banjarmasin Indonesia, ;
                Categories
                Original Article
                Custom metadata
                April-June 2023
                ts1

                nurse,occupational stressors,coping,meaning-based coping,stress,structural equation model

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