The health and care workforce crisis: co-benefits of gender-transformative approaches and capacities for implementation
Bibliographic record
Abstract
The health and care workforce crisis and gender inequalities are interconnected, threatening healthcare and equity. We turn this 'unhealthy' connection upside down, aiming to explore how health policy can create co-benefits for gender equality and how governance can support policy implementation. A conceptual approach on SDG3 'Health' and SDG5 'Gender Equality' co-benefits served our analysis. We applied a qualitative explorative approach to identify co-benefits; following a rapid scoping review of the literature, we focus on document analysis and an illustrative case study of artificial intelligence in the health and care workforce. The literature reveals an overall lack of attention to co-benefits in research. Policy documents pay some attention to co-benefits, but primarily consider the benefits for the healthcare sector rather than for gender equality. The case of artificial intelligence illustrates that technological innovations provide opportunities for change, but to create co-benefits, they need gender-responsive and equity-based policy approaches to enhance economically effective and socially fair transformations. We discuss how transformational leadership and gender-transformative governance approaches can support implementation of co-benefits policies. Strengthening the policy and implementation of co-benefits provides novel opportunities for improving gender equality and responding to the health and care workforce crisis.
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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 | 0.075 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.088 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.002 | 0.036 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".