Gunn Virginia, Muntaner Carles, Ng Edwin, Villeneuve Michael, Gea-Sanchez Montserrat, Chung Haejoo
Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.
Collaborative Doctoral Program in Global Health, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
J Adv Nurs. 2019 Nov;75(11):2797-2810. doi: 10.1111/jan.14155. Epub 2019 Aug 8.
The aim of this study was to examine the relationship between welfare states and nursing professionalization indicators.
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.
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.
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.
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.
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.
本研究旨在探讨福利国家与护理专业化指标之间的关系。
我们采用了时间序列横截面设计。分析涵盖了16年和22个国家：澳大利亚、奥地利、比利时、加拿大、丹麦、芬兰、法国、德国、希腊、爱尔兰、意大利、日本、荷兰、新西兰、挪威、葡萄牙、韩国、西班牙、瑞典、瑞士、英国和美国，这些国家被划分为五种福利国家体制：社会民主主义、基督教民主主义、自由主义、专制保守主义和儒家体制。
我们使用了固定效应线性回归模型，并进行了带有面板校正标准误差的Prais-Winsten回归，包括一阶自相关校正。我们应用了Amelia II多重插补策略来替换缺失的观测值。数据收集于2017年3月至12月，并于2018年8月至9月进行了更新。
我们的研究结果突出了注册护士与护士毕业生比例与教育、健康和家庭政策的福利国家措施之间的积极联系。此外，两个结果变量在福利国家体制之间的平均值存在差异，最低的是在专制保守主义体制中。
需要进一步开展国家层面和国际比较研究，以深入探讨护理专业化的一系列结构性政治和经济决定因素的影响。
我们研究了福利国家特征对护理专业化指标的影响，并发现有证据支持这些特征会影响注册护士与护士毕业生比例这一观点。这些研究结果可用于通过健康的公共政策加强护理工作和护理劳动力队伍，并提高卫生人力资源预测工具的准确性。
