Assessment of human resources for health programme implementation in 15 Latin American and Caribbean countries
Bibliographic record
Abstract
BACKGROUND: The health systems in the Americas region are characterized by fragmentation and segmentation, which constitute an important barrier to expanding coverage, achieving integrated primary health care, and reducing inefficiency and discontinuity of care. An assessment of the human resources for health (HRH) programmes that have been implemented at the country level was developed as part of the measurement of the 20 HRH regional goals for 2007-2015, adopted in 2007 by the Pan American Sanitary Conference (CSPA). METHODS: The exercise was a combination of academic research and the development/application of an advocacy tool involving policy makers and stakeholders to influence the decision-making in the development, implementation, or change of HRH programmes while building evidence through a structured approach based on qualitative and quantitative information and the exchange and dissemination of best practices. RESULTS: This paper covers the methodological challenges, as well as a summary of the main findings of the study, which included 15 countries: Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua and Panama in the Central America, Dominican Republic in the Caribbean, Chile, Colombia, Ecuador and Peru in the Andean sub region, and Argentina, Paraguay, and Uruguay in the South Cone. Despite the different contexts, the results showed that the programmes evaluated faced common challenges, such as lack of political support and financial unsustainability. CONCLUSIONS: The evaluation process allowed the exchange and dissemination of practices, interventions, and programmes currently running in the region. A shared lesson was the importance of careful planning of the implementation of programmes and interventions. The similarities in the problems and challenges of HRH among the participating countries highlighted the need for a cooperation programme on the evaluation and assessment of implementation strategies in the Americas region.
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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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".