Avançando no uso de políticas e práticas de saúde informadas por evidências: a experiência de Piripiri-Piauí
Bibliographic record
Abstract
Evidence-informed decision making (EIDM) can optimize health services and systems. EIDM involves defining problems, identifying measures to tackle them, assessing the quality of global and local evidence and translating it for the main stakeholders in line with social values and laws. Brazil encourages the use of EIDM in health policy in Piripiri, a municipality of 61,840 inhabitants in the country's poorest region, and launched Brazil's first Evidence Use in Health Care (NEv) center in 2010. The development and preliminary results of the NEv center are reported and its vision, composition, mandate, and activities are presented. The NEv center experience has the support of the Evidence-Informed Policy Network, the Latin American and Caribbean Center of Information on Health Sciences and federal and municipal governments. The decentralization of financing and the provision of healthcare services, the expansion of EIDM in management, and the local political context illustrate the progress of the experiment. Its activities include the production and dissemination of deliberative briefs and dialogues with opinion shapers, workers and health service users. Monitoring and evaluation are underway and the results will help to broaden the scale of activities in Brazil and abroad.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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; both teacher heads agree on what is shown here.
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".