Construyendo puentes entre investigación y políticas para la extensión de la protección social en salud en América Latina y el Caribe: una estrategia de cooperación conjunta Building bridges between research and policy to extend social protection in health in Latin America and the Caribbean: a strategy for cooperation
Bibliographic record
Abstract
Abstract The Pan American Health Organization (PAHO) and International Development Research Centre (IDRC) have promoted a joint initiative to design, implement, and evaluate innovative strategies for the Extension of Social Protection in Health (SPH) in Latin America and the Caribbean (LAC), involving active partnership between research-ers and research users. This initiative was based on a previous review of research on health sector reforms and the recommendations of the work-shop on “ Health Sector Reforms in the Ameri-cas: Strengthening the Links between Research and Policy ” (Montreal, Canada, 2001). In its first phase, the initiative supported the development of proposals aiming to extend SPH, elaborated jointly by researchers and decision-makers. In the second phase, the implementation of five of these proposals was supported in order to promote the development of new SPH strategies and new stakeholder interaction models. In this edition of the journal, the process of linking researchers and decision-makers will be analyzed in the context of the five projects supported by this initiative.Health Policy; Health Systems; Health Services
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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 | 0.166 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.045 | 0.023 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".