Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis

Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physi...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Basem Gohar [verfasserIn] Michel Larivière [verfasserIn] Nancy Lightfoot [verfasserIn] Céline Larivière [verfasserIn] Elizabeth Wenghofer [verfasserIn] Behdin Nowrouzi-kia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Meta-analysis Nursing Predictors Sickness Absenteeism

Übergeordnetes Werk:	In: Safety and Health at Work - Elsevier, 2017, 12(2021), 4, Seite 536-543
Übergeordnetes Werk:	volume:12 ; year:2021 ; number:4 ; pages:536-543

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.shaw.2021.07.006

Katalog-ID:	DOAJ004784588

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520			\|a Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures.
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allfields_unstemmed	10.1016/j.shaw.2021.07.006 doi (DE-627)DOAJ004784588 (DE-599)DOAJ7c5df2690c6e43078d09e90753526a69 DE-627 ger DE-627 rakwb eng RA1-1270 Basem Gohar verfasserin aut Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures. Meta-analysis Nursing Predictors Sickness Absenteeism Public aspects of medicine Michel Larivière verfasserin aut Nancy Lightfoot verfasserin aut Céline Larivière verfasserin aut Elizabeth Wenghofer verfasserin aut Behdin Nowrouzi-kia verfasserin aut In Safety and Health at Work Elsevier, 2017 12(2021), 4, Seite 536-543 (DE-627)641391161 (DE-600)2583825-8 20937997 nnns volume:12 year:2021 number:4 pages:536-543 https://doi.org/10.1016/j.shaw.2021.07.006 kostenfrei https://doaj.org/article/7c5df2690c6e43078d09e90753526a69 kostenfrei http://www.sciencedirect.com/science/article/pii/S2093791121000597 kostenfrei https://doaj.org/toc/2093-7911 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 4 536-543
allfieldsGer	10.1016/j.shaw.2021.07.006 doi (DE-627)DOAJ004784588 (DE-599)DOAJ7c5df2690c6e43078d09e90753526a69 DE-627 ger DE-627 rakwb eng RA1-1270 Basem Gohar verfasserin aut Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures. Meta-analysis Nursing Predictors Sickness Absenteeism Public aspects of medicine Michel Larivière verfasserin aut Nancy Lightfoot verfasserin aut Céline Larivière verfasserin aut Elizabeth Wenghofer verfasserin aut Behdin Nowrouzi-kia verfasserin aut In Safety and Health at Work Elsevier, 2017 12(2021), 4, Seite 536-543 (DE-627)641391161 (DE-600)2583825-8 20937997 nnns volume:12 year:2021 number:4 pages:536-543 https://doi.org/10.1016/j.shaw.2021.07.006 kostenfrei https://doaj.org/article/7c5df2690c6e43078d09e90753526a69 kostenfrei http://www.sciencedirect.com/science/article/pii/S2093791121000597 kostenfrei https://doaj.org/toc/2093-7911 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 4 536-543
allfieldsSound	10.1016/j.shaw.2021.07.006 doi (DE-627)DOAJ004784588 (DE-599)DOAJ7c5df2690c6e43078d09e90753526a69 DE-627 ger DE-627 rakwb eng RA1-1270 Basem Gohar verfasserin aut Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures. Meta-analysis Nursing Predictors Sickness Absenteeism Public aspects of medicine Michel Larivière verfasserin aut Nancy Lightfoot verfasserin aut Céline Larivière verfasserin aut Elizabeth Wenghofer verfasserin aut Behdin Nowrouzi-kia verfasserin aut In Safety and Health at Work Elsevier, 2017 12(2021), 4, Seite 536-543 (DE-627)641391161 (DE-600)2583825-8 20937997 nnns volume:12 year:2021 number:4 pages:536-543 https://doi.org/10.1016/j.shaw.2021.07.006 kostenfrei https://doaj.org/article/7c5df2690c6e43078d09e90753526a69 kostenfrei http://www.sciencedirect.com/science/article/pii/S2093791121000597 kostenfrei https://doaj.org/toc/2093-7911 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2021 4 536-543
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callnumber-first	R - Medicine
author	Basem Gohar
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topic_title	RA1-1270 Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis Meta-analysis Nursing Predictors Sickness Absenteeism
topic	misc RA1-1270 misc Meta-analysis misc Nursing misc Predictors misc Sickness Absenteeism misc Public aspects of medicine
topic_unstemmed	misc RA1-1270 misc Meta-analysis misc Nursing misc Predictors misc Sickness Absenteeism misc Public aspects of medicine
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title	Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis
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title_sort	demographic, lifestyle, and physical health predictors of sickness absenteeism in nursing: a meta-analysis
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title_auth	Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis
abstract	Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures.
abstractGer	Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures.
abstract_unstemmed	Background: Sickness absenteeism is an area of concern in nursing and is more concerning given the recent impacts of the COVID-19 pandemic on healthcare. This study is one of two meta-analyses that examined sickness absenteeism in nursing. In this study, we examined demographic, lifestyle, and physical health predictors. Methods: We reviewed five databases (CINAHL, ProQuest Allied, ProQuest database theses, PsycINFO, and PubMed) for our search. We registered the systematic review (CRD de-identified) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Additionally, we used the Population/Intervention/Comparison/Outcome Tool to improve our searches. Results: Following quality testing, 17 articles were used for quantitative synthesis. Female employees were at higher risks of sickness absenteeism than their male counterparts (OR = 1.73; 95% CI: 1.33–2.25). Nursing staff who rated their health as poor had a greater likelihood of experiencing sickness absence (OR = 1.38; 95% CI: 1.19-1.60). Also, previous sick leave predicted future leaves (OR = 3.35; 95% CI: 1.37–8.19). Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. More research is required to identify predictors of sickness absence, and thereby, implement preventative measures.
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title_short	Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis
url	https://doi.org/10.1016/j.shaw.2021.07.006 https://doaj.org/article/7c5df2690c6e43078d09e90753526a69 http://www.sciencedirect.com/science/article/pii/S2093791121000597 https://doaj.org/toc/2093-7911
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author2	Michel Larivière Nancy Lightfoot Céline Larivière Elizabeth Wenghofer Behdin Nowrouzi-kia
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Moreover, experiencing musculoskeletal pain (OR = 2.41 95% CI: 1.77–3.27) increased the likelihood of sickness absence with greater odds when it is a back pain (OR = 3.05; 95% CI: 1.66–5.62). Increased age, physical activity, and sleep were not associated with sick leave. Conclusion: Several variables were statistically associated with the occurrence of sickness absenteeism. One primary concern is the limited research in this area despite alarming rates of sick leave in healthcare. 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Demographic, Lifestyle, and Physical Health Predictors of Sickness Absenteeism in Nursing: A Meta-Analysis

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