Fatores impulsionadores e scorecards para melhorar o controle da hipertensão arterial na atenção primária: recomendações do Grupo de Inovação da Iniciativa HEARTS nas Américas
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the Americas, and hypertension is the most significant modifiable risk factor. However, hypertension control rates remain low, and CVD mortality is stagnant or rising after decades of continuing reduction. In 2016, the World Health Organization (WHO) launched the HEARTS technical package to improve hypertension control. The Pan American Health Organization (PAHO) designed the HEARTS in the Americas Initiative to improve CVD risk management, emphasizing hypertension control, to date implemented in 21 countries. Methods: To advance implementation, an interdisciplinary group of practitioners was engaged to select the key evidence-based drivers of hypertension control and to design a comprehensive scorecard to monitor their implementation at primary care health facilities (PHC). The group studied high-performing health systems that achieve high hypertension control through quality improvement programs focusing on specific process measures, with regular feedback to providers at health facilities. Findings: The final selected eight drivers were categorized into five main domains: (1) diagnosis (blood pressure measurement accuracy and CVD risk evaluation); (2) treatment (standardized treatment protocol and treatment intensification); (3) continuity of care and follow-up; (4) delivery system (team-based care, medication refill), and (5) system for performance evaluation. The drivers and recommendations were then translated into process measures, resulting in two interconnected scorecards integrated into the HEARTS in the Americas monitoring and evaluation system. Interpretation: Focus on these key hypertension drivers and resulting scorecards, will guide the quality improvement process to achieve population control goals at the participating health centers in HEARTS implementing countries.
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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 | 0.054 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".