Nursing professionalization and welfare state policies: A critical review of structural factors influencing the development of nursing and the nursing workforce
Bibliographic record
Abstract
Nursing professionalization is both ongoing and global, being significant not only for the nursing workforce but also for patients and healthcare systems. For this reason, it is important to have an in-depth understanding of this process and the factors that could affect it. This literature review utilizes a welfare state approach to examine macrolevel structural determinants of nursing professionalization, addressing a previously identified gap in this literature, and synthesizes research on the relevance of studying nursing professionalization. The use of a welfare state framework facilitates the understanding that the wider social, economic, and political system exercises significant power over the distribution of resources in a society, providing a glimpse into the complex politics of health and health care. The findings shed light on structural factors outside of nursing, such as country-level education, health, labor market, and gender policies that could impact the process of professionalization and thus could be utilized to strengthen nursing through facilitating increased professionalization levels. Addressing gender inequalities and other structural determinants of nursing professionalization could contribute to achieving health equity and could benefit health systems through enhanced availability, skill-level, and sustainability of nursing human resources, improved and efficient access to care, improved patient outcomes, and cost savings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".