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Record W2979196899 · doi:10.1590/s0104-12902019190060

Governance na saúde: os desafios da operacionalização

2019· article· pt· W2979196899 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSaúde e Sociedade · 2019
Typearticle
Languagept
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPolitical scienceCorporate governancePhilosophyManagementEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.014

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.

Opus teacher head0.073
GPT teacher head0.349
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it