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

被引:6
|
作者
Gunn, Virginia [1 ,2 ]
Muntaner, Carles [1 ,3 ]
Ng, Edwin [4 ]
Villeneuve, Michael [5 ]
Gea-Sanchez, Montserrat [6 ,7 ]
Chung, Haejoo [8 ,9 ]
机构
[1] Univ Toronto, Lawrence S Bloomberg Fac Nursing, Toronto, ON, Canada
[2] Univ Toronto, Dalla Lana Sch Publ Hlth, Collaborat Doctoral Program Global Hlth, Toronto, ON, Canada
[3] Univ Toronto, Dalla Lana Sch Publ Hlth, Toronto, ON, Canada
[4] Univ Waterloo, Renison Univ Coll, Sch Social Work, Waterloo, ON, Canada
[5] Canadian Nurses Assoc, Governance & Strategy, Ottawa, ON, Canada
[6] Univ Lleida, Fac Nursing & Physiotherapy, GESEC Grp, Lleida, Spain
[7] Biomed Res Inst Lleida, GRECS Grp, Lleida, Spain
[8] Korea Univ, Grad Sch, Dept Publ Hlth Sci, Seoul, South Korea
[9] Korea Univ, Sch Hlth Policy & Management, Coll Hlth Sci, Seoul, South Korea
关键词
gender equality policies; health human resources; nurses; midwives; nursing; nursing forecasting tools; nursing professionalization; patient and health system outcomes; politics of health; structural political and economic factors; time-series cross-sectional design; welfare state regimes and policy; GENDER INEQUALITIES; HEALTH; EDUCATION; POLITICS; REGIME; SYSTEMS; CARE;
D O I
10.1111/jan.14155
中图分类号
R47 [护理学];
学科分类号
1011 ;
摘要
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.
引用
收藏
页码:2797 / 2810
页数:14
相关论文
共 50 条
  • [1] Gender equality policies, nursing professionalization, and the nursing workforce: A cross-sectional, time-series analysis of 22 countries, 2000-2015
    Gunn, Virginia
    Muntaner, Caries
    Ng, Edwin
    Villeneuve, Michael
    Gea-Sanchez, Montserrat
    Chung, Haejoo
    INTERNATIONAL JOURNAL OF NURSING STUDIES, 2019, 99
  • [2] Increasing implicit rationing of care in nursing homes: A time-series cross-sectional analysis
    Renner, Anja
    Ausserhofer, Dietmar
    Zuniga, Franziska
    Simon, Michael
    Serdaly, Christine
    Favez, Lauriane
    INTERNATIONAL JOURNAL OF NURSING STUDIES, 2022, 134
  • [4] Influence of Nursing Time and Staffing on Medication Errors: A Cross-Sectional Analysis of Administrative Data
    Moriwaki, Mutsuko
    Tanaka, Michiko
    Kakehashi, Masayuki
    Koizumi, Masato
    Horiguchi, Hiromasa
    Hayashida, Kenshi
    NURSING REPORTS, 2025, 15 (01)
  • [5] Mobbing among care workers in nursing homes: A cross-sectional secondary analysis of the Swiss Nursing Homes Human Resources Project
    Tong, Myriam
    Schwendimann, Rene
    Zuniga, Franziska
    INTERNATIONAL JOURNAL OF NURSING STUDIES, 2017, 66 : 72 - 81
  • [6] POOLING CROSS-SECTIONAL AND TIME-SERIES YIELD DATA FOR RISK ANALYSIS
    ATWOOD, J
    HELMERS, GA
    ESKRIDGE, KM
    MORRILL, JM
    LANGEMEIER, MR
    AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS, 1986, 68 (05) : 1385 - 1385
  • [7] CROSS-SECTIONAL, TIME-SERIES ISSUES IN ANALYSIS OF MARKETING DECISION VARIABLES
    MORIARTY, M
    JOURNAL OF MARKETING RESEARCH, 1975, 12 (02) : 142 - 150
  • [8] Uncovered Interest Parity Puzzle: Cross-sectional and Time-series Analysis
    Lee, Byung-Loo
    2007 KDI-KAEA CONFERENCE ON ENHANCING PRODUCTIVITY AND SUSTAINING GROWTH, 2007, : 266 - 292
  • [9] MODELS OF COMPARATIVE STATE POLITICS - COMPARISON OF CROSS-SECTIONAL AND TIME-SERIES ANALYSES
    GRAY, V
    AMERICAN JOURNAL OF POLITICAL SCIENCE, 1976, 20 (02) : 235 - 256
  • [10] Uneven state distribution of homicides in Brazil and their effect on life expectancy, 2000-2015: a cross-sectional mortality study
    Aburto, Jose Manuel
    Calazans, Julia
    Lanza Queiroz, Bernardo
    Luhar, Shammi
    Canudas-Romo, Vladimir
    BMJ OPEN, 2021, 11 (02):