foco nos estudos de implementação para avaliação do Plano Estratégico de Cooperação em Saúde (PECS)
Bibliographic record
Abstract
O objetivo geral deste artigo é apresentar uma proposta preliminar de estraté- gia para fortalecimento da capacidade avaliativa nos países da CPLP. Esta abordagem permitirá estimar o grau de implementação do PECS, contextualizado nacionalmente, bem como favorecer a institucionalização do monitoramento e avaliação de desempenho dos diferentes níveis dos sistemas de saúde. A proposta fundamenta-se em mais de duas décadas de experiência na investigação e formação pós-graduada de avaliadores, com base na tipologia de avaliação do Grupo Pesquisa Interdisciplinar de Saúde da Universidade de Montreal, devidamente documentada no Brasil em cinco livros da Editora Fiocruz e diversas edições temáticas das principais revistas de saúde pública.
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.
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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".