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Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis
This study examines the association of sleep quality with job burnout among Chinese coal mine staff. 3832 subjects were selected from a coal mine group located in Shanxi Province in China. Job burnout was evaluated by the Maslach Burnout Inventory-General Survey and sleep quality was acquired with a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584660/ https://www.ncbi.nlm.nih.gov/pubmed/31217549 http://dx.doi.org/10.1038/s41598-019-45329-2 |
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author | Gao, Xue Ma, Kai-Li Wang, Hui Gao, Qian Lei, Li-Jian Wang, Tong |
author_facet | Gao, Xue Ma, Kai-Li Wang, Hui Gao, Qian Lei, Li-Jian Wang, Tong |
author_sort | Gao, Xue |
collection | PubMed |
description | This study examines the association of sleep quality with job burnout among Chinese coal mine staff. 3832 subjects were selected from a coal mine group located in Shanxi Province in China. Job burnout was evaluated by the Maslach Burnout Inventory-General Survey and sleep quality was acquired with a self-reported questionnaire. We used the inverse probability of treatment weighting with propensity score to mimic the randomization and to minimize bias in estimations. Sensitivity analysis was conducted to test the robustness of our findings. We identified that good sleep quality was significantly associated with lower risk of job burnout (OR: 0.70; 95%CI, 0.60 to 0.82, p = 6.02e-06), with 0.21 decrease in the score of exhaustion (95%CI,−0.29 to −0.12, p = 5.00e-06), and with 0.13 decrease in the score of cynicism (95%CI,−0.21 to −0.04, p = 3.73e-03). Sensitivity analysis demonstrated that the results were robust to the choice of estimation models, as well as unmeasured confounding. Stratification analysis demonstrated that the associations of sleep quality with job burnout were largely heterogeneous for male and female workers. This study implicated that good sleep quality benefits the workers in relief of job burnout. Further research may be warranted in support of a definite causal relationship and intervention strategy. |
format | Online Article Text |
id | pubmed-6584660 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-65846602019-06-26 Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis Gao, Xue Ma, Kai-Li Wang, Hui Gao, Qian Lei, Li-Jian Wang, Tong Sci Rep Article This study examines the association of sleep quality with job burnout among Chinese coal mine staff. 3832 subjects were selected from a coal mine group located in Shanxi Province in China. Job burnout was evaluated by the Maslach Burnout Inventory-General Survey and sleep quality was acquired with a self-reported questionnaire. We used the inverse probability of treatment weighting with propensity score to mimic the randomization and to minimize bias in estimations. Sensitivity analysis was conducted to test the robustness of our findings. We identified that good sleep quality was significantly associated with lower risk of job burnout (OR: 0.70; 95%CI, 0.60 to 0.82, p = 6.02e-06), with 0.21 decrease in the score of exhaustion (95%CI,−0.29 to −0.12, p = 5.00e-06), and with 0.13 decrease in the score of cynicism (95%CI,−0.21 to −0.04, p = 3.73e-03). Sensitivity analysis demonstrated that the results were robust to the choice of estimation models, as well as unmeasured confounding. Stratification analysis demonstrated that the associations of sleep quality with job burnout were largely heterogeneous for male and female workers. This study implicated that good sleep quality benefits the workers in relief of job burnout. Further research may be warranted in support of a definite causal relationship and intervention strategy. Nature Publishing Group UK 2019-06-19 /pmc/articles/PMC6584660/ /pubmed/31217549 http://dx.doi.org/10.1038/s41598-019-45329-2 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Gao, Xue Ma, Kai-Li Wang, Hui Gao, Qian Lei, Li-Jian Wang, Tong Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis |
title | Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis |
title_full | Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis |
title_fullStr | Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis |
title_full_unstemmed | Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis |
title_short | Association of sleep quality with job burnout among Chinese coal mine staff: a propensity score weighting analysis |
title_sort | association of sleep quality with job burnout among chinese coal mine staff: a propensity score weighting analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584660/ https://www.ncbi.nlm.nih.gov/pubmed/31217549 http://dx.doi.org/10.1038/s41598-019-45329-2 |
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