Participatory health councils and good governance: healthy democracy in Brazil?
Bibliographic record
Abstract
INTRODUCTION: The Brazilian Government created Participatory Health Councils (PHCs) to allow citizen participation in the public health policy process. PHCs are advisory bodies that operate at all levels of government and that bring together different societal groups to monitor Brazil's health system. Today they are present in 98% of Brazilian cities, demonstrating their popularity and thus their potential to help ensure that health policies are in line with citizen preferences. Despite their expansive reach, their real impact on health policies and health outcomes for citizens is uncertain. We thus ask the following question: Do PHCs offer meaningful opportunities for open participation and influence in the public health policy process? METHODS: Thirty-eight semi-structured interviews with health council members were conducted. Data from these interviews were analyzed using a qualitative interpretive content analysis approach. A quantitative analysis of PHC data from the Sistema de Acompanhamento dos Conselhos de Saude (SIACS) database was also conducted to corroborate findings from the interviews. RESULTS: We learned that PHCs fall short in many of the categories of good governance. Government manipulation of the agenda and leadership of the PHCs, delays in the implementation of PHC decision making, a lack of training of council members on relevant technical issues, the largely narrow interests of council members, the lack of transparency and monitoring guidelines, a lack of government support, and a lack of inclusiveness are a few examples that highlight why PHCs are not as effective as they could be. CONCLUSIONS: Although PHCs are intended to be inclusive and participatory, in practice they seem to have little impact on the health policymaking process in Brazil. PHCs will only be able to fulfil their mandate when we see good governance largely present. This will require a rethinking of their governance structures, processes, membership, and oversight. If change is resisted, the PHCs will remain largely limited to a good idea in theory that is disappointing in practice.
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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 | 0.038 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".