Mapping stages, barriers and facilitators to the implementation of HEARTS in the Americas initiative in 12 countries: A qualitative study
Bibliographic record
Abstract
The World Health Organization (WHO) Global Hearts Initiative offers technical packages to reduce the burden of cardiovascular diseases through population-wide and targeted health services interventions. The Pan American Health Organization (PAHO) has led implementation of the HEARTS in the Americas Initiative since 2016. The authors mapped the developmental stages, barriers, and facilitators to implementation among the 371 primary health care centers in the participating 12 countries. The authors used the qualitative method of document review to examine cumulative country reports, technical meeting notes, and reports to regional stakeholders. Common implementation barriers include segmentation of health systems, overcoming health care professionals' scope of practice legal restrictions, and lack of health information systems limiting operational evaluation and quality improvement mechanisms. Main implementation facilitators include political support from ministries of health and leading scientific societies, PAHO's role as a regional catalyst to implementation, stakeholder endorsement demonstrated by incorporating HEARTS into official documents, and having a health system oriented to primary health care. Key lessons include the need for political commitment and cultivating on-the-ground leadership to initiate a shift in hypertension care delivery, accompanied by specific progress in the development of standardized treatment protocols and a set of high-quality medicines. By systematizing an implementation strategy to ease integration of interventions into delivery processes, the program strengthened technical leadership and ensured sustainability. These study findings will aid the regional approach by providing a staged planning model that incorporates lessons learned. A systematic approach to implementation will enhance equity, efficiency, scale-up, and sustainability, and ultimately improve population hypertension control.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".