Indicadores de saúde no Brasil: um processo em construção Health indicators in Brazil: a process under construction
Bibliographic record
Abstract
RESUMO Nos ultimos anos, o uso de indicadores de saude tem crescido de uma forma exponencial. Nunca se falou tanto em medir estados de saude e comparar desempenhos de sistemas de saude como agora. Paralelamente a essa tendencia de uso crescente, impoe-se a necessidade de um conhecimento mais cientifico e aprofundado desses instrumentos. Este artigo tem por objetivo reunir algumas informacoes basicas sobre indicadores e disponibiliza-las de uma forma estruturada e objetiva para os profissionais e pesquisadores que estejam iniciando a sua viagem pelo processo de aprendizagem e aplicacao dessa ferramenta. Este trabalho e dividido em duas partes principais. Iniciamos com uma explanacao sobre os conceitos, os processos de construcao e selecao e as caracteristicas fundamentais dos indicadores. Na segunda parte, apresentamos de forma sucinta e concisa as matrizes de indicadores do Brasil, Canada, Australia, Reino Unido e Estados Unidos da America. Longe de esgotar o tema, gostariamos de desencadear uma discussao organizada e o mais diversificada possivel sobre o assunto.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".