The influence of welfare state factors on nursing professionalization and nursing human resources: A time‐series cross‐sectional analysis, 2000–2015
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".