How do high-level African directors of policy and planning operationalize health equity? Findings from a regional survey
Bibliographic record
Abstract
This study investigates the operationalization of health equity and associated challenges faced by directors of policy and planning and those in related positions in the African Region. The results of this study demonstrate that health equity is generally a priority for policymakers in practice and is operationalized in four ways: (i) targeting priority groups, (ii) focusing on the entire population; (iii) through procedural justice; and (iv) operationalizing population health data. The targeted approach, which predominated, tended to focus on individuals with lower socioeconomic status, maternal and child health services, and select infectious diseases (e.g., HIV/AIDS). The population-wide approach entailed the inclusion of health equity in institutional laws, constitutions, and national policies with efforts to ensure access to health services for all. Procedural justice largely focused on the inclusion of stakeholders in decision-making processes. Lastly, operationalizing population health data was noted to guide policy and planning to address health inequities, often through aiding in the selection of priority groups or areas for intervention and monitoring and evaluation actions focused on improving health equity. Four main domains of challenges for incorporating health equity emerged relating to: (i) understanding health equity, (ii) governance, (iii) resources, and (iv) lack of data. Our recommendations are two-fold: (i) we recommend that researchers focus on improving understandings of health equity among policymakers through knowledge translation and exchange, and (ii) we recommend that policymakers and those working within donor organizations focus on reforming any top-down processes through which priorities are set and decisions are made.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".