Implementing the Agenda for Global Action on human resources for health: analysis from an international tracking survey
Bibliographic record
Abstract
Objective:A survey was conducted to analyse the governance and policy environment for human resources for health (HRH) development in 57 priority countries, with the objective of understanding the linkages between policy and context factors.Methods:Responses to a questionnaire tracking proxy indicators were received from 51 (89%) countries. Findings are presented by frequency; correlations were investigated through cross tabulations and multiple regression analysis.Results:The results indicate uneven performance among countries and across different domains of health workforce development. The only indicator showing a significant correlation with other areas of performance was implementing an HRH plan. No significant correlation with contextual factors was found.Discussion:Progress in addressing HRH challenges appears to be independent of contextual factors, suggesting that countries can improve their performance through concerted action by stakeholders. Having and implementing an HRH plan appears to be a key factor in galvanising that action.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".