Estudo de avaliabilidade do programa de indução à pesquisa em saúde no Brasil para mitigar problemas relacionados à extrema pobreza
Bibliographic record
Abstract
The multidimensionality of poverty was the central premise of the Brazilian Without Misery Plan (2011-2014), which aimed at eradicating misery in the country. The Oswaldo Cruz Foundation coordinated a health research induction program that promoted research aimed at producing knowledge to mitigate problems related to misery. The evaluability study was guided by the theoretical and methodological instrument of the Theory of Change, which allowed us to trace the path from the initial scenario to an expected scenario, based on the induction results, represented in the logical model of the program, in which the structure, processes, and results were also described. Through document analysis and interviews with stakeholders, it was possible to identify the objectives, expected results of the program, as well as the public benefited by it and by the evaluation of the program’s results. This study guided the planning of the assessment that followed, as well as the definition of the Canadian model, to measure the return on health research investment as a methodological strategy. The limitations were recognized, as well as the issues that favored the implementation and development of the induction program. It is further hoped that this study may contribute to evaluations and/or pre-evaluations of other similarly targeted programs.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".