Intersectoral action for health at a municipal level in Cuba
Bibliographic record
Abstract
OBJECTIVE: To consider how Cuba's acknowledged achievement of excellent health outcomes may relate to how health determinants are addressed intersectorally. METHODS: Our team of Canadian and Cuban researchers and health policy practitioners undertook a study to consider the organization and practices involved in addressing health determinants in 2 municipalities (1 urban and 1 rural). The study included a questionnaire of municipal Health Council members and others involved in health and non-health sectors, key informant interviews of policy makers, focus groups in each municipality and examination of three common case scenarios. RESULTS: Regular engagement of different sectors and other agencies in addressing health determinants was quite systematic and comparable in both municipalities. Specific policies and organizational structures in support of intersectoral actions were frequently cited and illustrated in case scenarios that demonstrate how maintenance of regular linkages facilitates regular pursuit of intersectoral approaches. CONCLUSIONS: The study demonstrates the feasibility of examining processes of intersectoral action for health processes and suggests that further examination in evaluating factors such as training, particular practices, etc., can be a fruitful direction to pursue comparatively and with analytical designs.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".