Using health committees to promote community participation as a social determinant of the right to health : lessons from Uuganda and South Africa
Bibliographic record
Abstract
Community participation is not only a human right in itself but an essential underlying determinant for realizing the right to health, since it enables communities to be active and informed participants in the creation of a responsive health system that serves them efficiently. As acknowledged by the Rio Political Declaration on Social Determinants of Health, participatory processes are important in policymaking and in the implementation of laws relating to health. Collective deliberation improves both community development and health system governance, resulting in more reasoned, informed, and public-oriented decisions. 1 More recently, attention has focused on the elements of health system governance that enable greater responsiveness to community needs. However, there is relatively little by way of interventions linking human rights approaches to governance in ways that recognize participation as a critical social determinant of the right to health. This paper provides perspectives from a three-year intervention whose general objective was to develop and test models of good practice for health committees in South Africa and Uganda. It describes the aspects that we found critical for enhancing the potential of such committees in driving community participation as a social determinant of the right to 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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".