Governance na saúde: os desafios da operacionalização
Bibliographic record
Abstract
Resumo A massificação dos conceitos em geral torna-os, muitas vezes, difíceis de precisar. O conceito de governance tornou-se transversal a várias áreas, sendo orientado de acordo com a área em que é aplicado. Autores referem que a governance surge como um “chapéu” sob o qual se encaixam muitos temas, motivo pelo qual surgiram diversos conceitos, com influência das áreas em que eram aplicados. Embora pesem as diversas traduções para a língua portuguesa encontradas na literatura, de forma genérica, o termo “governance” pode ser entendido como um modelo de governação em rede. Este trabalho pretende percorrer as diversas definições de governance, governance associada ao setor da saúde e, dentro deste, os diversos conceitos de governance encontrados na literatura. O objetivo é perceber quais são os fatores que dificultam a operacionalização da governance na saúde. São descritos fatores que de forma persistente condicionam a operacionalização da governance. O desafio é encontrar formas inovadoras para conseguir atenuar o impacto desses fatores.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".