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Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018

OBJECTIVES: Given the increased ageing population and frequent epidemic challenges, it is vital to have the nurse workforce of sufficient quantity and quality. This study aimed to demonstrate the trends, composition and distribution of nurse workforce in China. DESIGN: Secondary analysis using natio...

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Autores principales: Lu, Han, Hou, Luoya, Zhou, Weijiao, Shen, Liqiong, Jin, Shida, Wang, Mengqi, Shang, Shaomei, Cong, Xiaomei, Jin, Xiaoyan, Dou, Dou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552175/
https://www.ncbi.nlm.nih.gov/pubmed/34706946
http://dx.doi.org/10.1136/bmjopen-2020-047348
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author Lu, Han
Hou, Luoya
Zhou, Weijiao
Shen, Liqiong
Jin, Shida
Wang, Mengqi
Shang, Shaomei
Cong, Xiaomei
Jin, Xiaoyan
Dou, Dou
author_facet Lu, Han
Hou, Luoya
Zhou, Weijiao
Shen, Liqiong
Jin, Shida
Wang, Mengqi
Shang, Shaomei
Cong, Xiaomei
Jin, Xiaoyan
Dou, Dou
author_sort Lu, Han
collection PubMed
description OBJECTIVES: Given the increased ageing population and frequent epidemic challenges, it is vital to have the nurse workforce of sufficient quantity and quality. This study aimed to demonstrate the trends, composition and distribution of nurse workforce in China. DESIGN: Secondary analysis using national public datasets in China from 2003 to 2018. SETTING/PARTICIPANTS: National population, nurse workforce and physician workforce. PRIMARY AND SECONDARY OUTCOME MEASURES: Frequency and proportion were used to demonstrate: (1) the longitudinal growth of nurse workforce; (2) the diversity of nurse workforce in gender, age, work experience and education level; and (3) the distribution of nurse workforce among provinces, rural–urban areas and hospital/community settings. The Gini coefficient and Theil L index were used to measure the inequality trends of nurse workforce. RESULTS: The total number of nurses increased from 1.3 million to 4.1 million and the density increased from 1 to 2.94 per 1000 population over 2003–2018. The nurses to physician ratio changed from 0.65:1 to 1.14:1. The majority of the nurse workforce was female, under 35 years old, with less than 30 years of work experience, with an associate’s degree and employed within hospitals. Central and eastern regions had more nurses and there were 5.08 nurses per 1000 population in urban areas while less than two in rural areas in 2018. The Gini coefficient and between-provincial Theil index experienced a consistent decline. Within-province inequality accounted for overall inequality has risen from 52.38% in 2010 to 71.43% in 2018 suggested that the differences of distribution are mainly reflected in urban and rural areas. CONCLUSION: Chinese nurse workforce has been changed significantly in the past 15 years that may be associated with the reformations of policy, nursing education in China. Our study suggests current features in the nurse workforce and can be used to strengthen future health services.
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spelling pubmed-85521752021-11-10 Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018 Lu, Han Hou, Luoya Zhou, Weijiao Shen, Liqiong Jin, Shida Wang, Mengqi Shang, Shaomei Cong, Xiaomei Jin, Xiaoyan Dou, Dou BMJ Open Health Policy OBJECTIVES: Given the increased ageing population and frequent epidemic challenges, it is vital to have the nurse workforce of sufficient quantity and quality. This study aimed to demonstrate the trends, composition and distribution of nurse workforce in China. DESIGN: Secondary analysis using national public datasets in China from 2003 to 2018. SETTING/PARTICIPANTS: National population, nurse workforce and physician workforce. PRIMARY AND SECONDARY OUTCOME MEASURES: Frequency and proportion were used to demonstrate: (1) the longitudinal growth of nurse workforce; (2) the diversity of nurse workforce in gender, age, work experience and education level; and (3) the distribution of nurse workforce among provinces, rural–urban areas and hospital/community settings. The Gini coefficient and Theil L index were used to measure the inequality trends of nurse workforce. RESULTS: The total number of nurses increased from 1.3 million to 4.1 million and the density increased from 1 to 2.94 per 1000 population over 2003–2018. The nurses to physician ratio changed from 0.65:1 to 1.14:1. The majority of the nurse workforce was female, under 35 years old, with less than 30 years of work experience, with an associate’s degree and employed within hospitals. Central and eastern regions had more nurses and there were 5.08 nurses per 1000 population in urban areas while less than two in rural areas in 2018. The Gini coefficient and between-provincial Theil index experienced a consistent decline. Within-province inequality accounted for overall inequality has risen from 52.38% in 2010 to 71.43% in 2018 suggested that the differences of distribution are mainly reflected in urban and rural areas. CONCLUSION: Chinese nurse workforce has been changed significantly in the past 15 years that may be associated with the reformations of policy, nursing education in China. Our study suggests current features in the nurse workforce and can be used to strengthen future health services. BMJ Publishing Group 2021-10-27 /pmc/articles/PMC8552175/ /pubmed/34706946 http://dx.doi.org/10.1136/bmjopen-2020-047348 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Health Policy
Lu, Han
Hou, Luoya
Zhou, Weijiao
Shen, Liqiong
Jin, Shida
Wang, Mengqi
Shang, Shaomei
Cong, Xiaomei
Jin, Xiaoyan
Dou, Dou
Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018
title Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018
title_full Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018
title_fullStr Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018
title_full_unstemmed Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018
title_short Trends, composition and distribution of nurse workforce in China: a secondary analysis of national data from 2003 to 2018
title_sort trends, composition and distribution of nurse workforce in china: a secondary analysis of national data from 2003 to 2018
topic Health Policy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8552175/
https://www.ncbi.nlm.nih.gov/pubmed/34706946
http://dx.doi.org/10.1136/bmjopen-2020-047348
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