MEASUREMENT AND ANALYSIS TO APPROACH INEQUALITIES AND BUILD ACCOUNTABILITY ON HEALTH POLICIES IN BRAZIL
Bibliographic record
Abstract
Health inequalities have been broadly documented especially by the Commission on Social Determinants of Health. To reduce their impact on health outcomes, policy encompassed intersectoral approach should be taken into consideration. This paper draws on the recommendations of the Rio Political Declaration on Social Determinants of Health (2011) specifically on monitoring inequalities and accountability, which is one of the themes of the conference. The paper is divided into four sections: brief history of the social determinants, measuring and monitoring inequalities and accountability to reduce health inequalities. Finally, obesity is taken as an example to clarify how to use monitoring and accountability to design strategies and inform policies. It concludes by emphasizing the need for adequate information in terms of equity to monitor health inequalities. There is a lack of indicators on the impact of different policies on social determinants of health. This requires a high degree of political commitment from all sectors of society, as well as to strengthen citizen participation to enhance accountability and promote health equity nationally and globally.
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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 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".