Using law to strengthen health professions: frameworks and practice
Bibliographic record
Abstract
The lack of sufficient, high-quality health workers is one of the primary barriers to improving health in sub-saharan africa. An approach to address this challenge is for public health practitioners to increase their cooperation with public health lawyers, regulators and other policymakers in order to develop strengthened health workforce laws, regulations, and policies that are vigorously implemented and enforced. Conceptual frameworks can help clarify the meaning of health system governance and the pathways between law and health. International recommendations for policy interventions governing health workers provide countries with valuable guidance for domestic reforms. Monitoring and evaluation of legal, regulatory, and other policy interventions are required to ascertain their public health impact. At the intersection of law and public health, professionals from both fields can collaborate in concrete ways such as those discussed here to improve laws and policies governing health.
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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 | 0.094 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.104 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".