The influence of welfare state factors on nursing professionalization and nursing human resources: A time‐series cross‐sectional analysis, 2000–2015

Abstract

Aim The aim of this study was to examine the relationship between welfare states and nursing professionalization indicators. Design We used a time‐series, cross‐sectional design. The analysis covered 16 years and 22 countries: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, South Korea, Spain, Sweden, Switzerland, United Kingdom, and the United States, allocated to five welfare state regimes: Social Democratic, Christian Democratic, Liberal, Authoritarian Conservative, and Confucian. Methods We used fixed‐effects linear regression models and conducted Prais‐Winsten regressions with panel‐corrected standard errors, including a first‐order autocorrelation correction. We applied the Amelia II multiple imputation strategy to replace missing observations. Data were collected from March–December 2017 and subsequently updated from August–September 2018. Results Our findings highlight positive connections between the regulated nurse and nurse graduate ratios and welfare state measures of education, health, and family policy. In addition, both outcome variables had averages that differed among welfare state regimes, the lowest being in Authoritarian Conservative regimes. Conclusion Additional country‐level and international comparative research is needed to further study the impact of a wide range of structural political and economic determinants of nursing professionalization. Impact We examined the effects of welfare state characteristics on nursing professionalization indicators and found support for the claim that such features affect both the regulated nurse and nurse graduate ratios. These findings could be used to strengthen nursing and the nursing workforce through healthy public policies and increase the accuracy of health human resources forecasting tools.

Employment and working conditions of nurses: where and how health inequalities have increased during the COVID-19 pandemic?

“…There is evidence of numerous links between the characteristics of welfare state regimes and the regulation of nurse and nursing professionalization, suggesting that the political context has to be acknowledged and addressed to significantly influence nursing employment and working conditions and health policy [ 86 , 87 ]. In addition, the presence of a Government Chief Nursing Officer (GCNO) position and the existence of a nursing leadership programme to effectively take action in government actions, are associated with a stronger regulation of employment and working conditions for nurses and regulation of nursing education [ 12 ].…”

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

“…To the best of our knowledge, this is the first study to report trends of the nurse workforce on a large scale, national level and over the past 15 years of period. As in many ways, the nursing human resources and education, and health and family policy all impact each other, 21 the results of the analysis in this article can be used for policy-making, The number of Chinese nurse workforce in 2017 (3.8 million) accounted for 18% of the nurse workforce in the world (20.7 million), and this number increased to 4.1 million in 2018. 22 The Chinese nurses per 1000 population were 2.94 in 2018, exceeding the minimum standard set by WHO (2 nurses per 1000 populations) and some developing countries (eg, India and South Africa), yet this density is still relatively below the average density of Organisation for Economic Co-operation and Development member countries (9 nurses per 1000 populations and 50% of WHO Member States in 2017).…”

“…To the best of our knowledge, this is the first study to report trends of the nurse workforce on a large scale, national level and over the past 15 years of period. As in many ways, the nursing human resources and education, and health and family policy all impact each other, 21 the results of the analysis in this article can be used for policy-making, healthcare administration, education and further research to strengthen nurse workforce.…”

Nursing‐sensitive quality indicators for pernicious placenta previa in obstetrics: A Delphi study based across Chinese institutions

“…In this study, structural indicators include three secondary indicators: organization and personnel, training and education, medical materials and equipment. A large number of studies have shown that the allocation of nursing human resources has a direct impact on nursing outcomes (Gunn et al., 2019 ; Han et al., 2018 ). Therefore, the nurse–patient ratio and the bed‐nurse ratio become important sensitive indicators.…”