The Guyana Program to Advance Cardiac Care: A Model for Equitable Cardiovascular Care Delivery
Bibliographic record
Abstract
Guyana is one of the poorest countries in South America, with the highest rate of cardiovascular mortality on the continent. As is the case in many low- and middle-income countries, cardiovascular care is available through the private sector but is not accessible to much of the urban and rural poor. We present the 10-year experience of the Guyana Program to Advance Cardiac Care (GPACC), an academic partnership aiming to provide high-quality, equitable cardiovascular care in Georgetown's only public hospital. We discuss the implementation of a cardiac care program using the World Health Organization Framework for Action, outlining vital components for care delivery in resource-limited settings. GPACC was able to demonstrate that targeted investment, education of clinicians, and cohesive healthcare delivery strategies can contribute to sustainable service delivery for Guyana's largest burden of disease. This structured approach may provide lessons for implementation of similar programs in other resource-limited settings. Highlights: In many LMICs, specialized cardiovascular care is available in the private, but not public, sector.The WHO Framework for Action can guide development of sustainable programs in low-resource settings.GPACC can serve as a successful and innovative model for delivery of sustainable cardiovascular care.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".