Transforming Health Workers’ Education for Universal Health Coverage: Global Challenges and Recommendations
Bibliographic record
Abstract
Health workforce challenges remain a critical bottleneck in achieving universal health coverage (UHC) goals in most countries. As it stands, health professional training is primarily clinical, curricular and delinked from the needs of the health system. To achieve global health goals and maximize opportunities for employment and economic growth, all in the context of limited fiscal realities, a paradigm shift is needed with respect to the health workforce and corresponding education systems. There is a need to shift towards fair, gender friendly employment at a rate that matches the overall growth of the health economy, which acknowledges the role of the private sector in education and training. This paper emphasizes the importance and implications of such a paradigm shift. It argues the need for a 21st century framework for health professional education. This framework should represent a more satisfactory interface between supply and demand for health professional labor, in line with the need for UHC, job creation and economic growth.
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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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".