Equity-oriented toolkit for health technology assessment and knowledge translation: application to scaling up of training and education for health workers
Bibliographic record
Abstract
Human resources for health are in crisis worldwide, especially in economically disadvantaged areas and areas with high rates of HIV/AIDS in both health workers and patients. International organizations such as the Global Health Workforce Alliance have been established to address this crisis. A technical working group within the Global Health Workforce Alliance developed recommendations for scaling up education and training of health workers. The paper will illustrate how decision-makers can use evidence and tools from an equity-oriented toolkit to scale up training and education of health workers, following five recommendations of the technical working group. The Equity-Oriented Toolkit, developed by the World Health Organization Collaborating Centre for Knowledge Translation and Health Technology Assessment in Health Equity, has four major steps: (1) burden of illness; (2) community effectiveness; (3) economic evaluation; and (4) knowledge translation/implementation. Relevant tools from each of these steps will be matched with the appropriate recommendation from the technical working group.
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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 | 0.014 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".