[A framework for evaluating primary health care in Latin America].
Bibliographic record
Abstract
OBJECTIVES: To determine the relevancy of applying the Canadian primary health care (PHC) assessment strategy to Latin America and to propose any modifications that might be needed for reaching a consensus in Latin America. METHODS: The Delphi method was used to reach a consensus among 29 experts engaged in PHC development or evaluation in Latin America. Four virtual sessions and a face-to-face meeting were held to discuss the PHC evaluation logic model and the seven goals and six conditioning factors that make up the Canadian strategy, as well as any questions regarding the evaluation and indicators. The relevance of each concept was ranked according to the perspective of the Latin American countries. RESULTS: The experts considered the Canadian strategy's objectives and conditioning factors to be highly relevant to assessing PHC in Latin America, though they acknowledged that additional modification would increase relevance. The chief suggestions were to create a PHC vision and mission, to include additional objectives and conditioning factors, and to rework the original set. The objectives that concerned coordination and integrated comprehensive care did not achieve a high degree of consensus because of ambiguities in the original text and multiple interpretations of statements regarding certain aspects of the evaluation. CONCLUSIONS: Considerable progress was made on the road to building a PHC evaluation framework for the Region of the Americas. Indicators and information-gathering tools, which can be appropriately and practically applied in diverse contexts, need to be developed.
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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 | 0.074 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".