Cardiac Rehabilitation Delivery in Low and Middle-Income Countries
Bibliographic record
Abstract
Cardiovascular diseases are among the leading causes of disability in low- and middle-income countries (LMICs). Cardiac rehabilitation (CR) is an effective secondary prevention program model. In this cross-sectional study, a confidential, online surveywas administered to CR programs around the world. CR programs were identified in 55/138 (39.9%) LMICs; 47 (85.5% country response rate) countries participated and 335(53.5% program response rate) surveys were initiated. There was 1 CR spot for every66 incident ischemic heart disease patients in LMICs. CR was most often paid by patients in LMICs (n=212,65.0%). On average, programs offered 7.31.8/11 core components over 33.730.7 sessions (significantly greater in publicly-funded programs;p<.001). Lack of patient referral (3.8/5) and financial resources (3.5/5) were the greatest barriers to CR provision in LMICs. CR is only available in 40% of LMICs, but where offered is fairly consistent with CR guidelines. Governments must enact policies to reimburse CR so patients do notCardiovascular diseases are among the leading causes of disability in low- and middle-income countries (LMICs). Cardiac rehabilitation (CR) is an effective secondary prevention program model. In this cross-sectional study, a confidential, online surveywas administered to CR programs around the world. CR programs were identified in 55/138 (39.9%) LMICs; 47 (85.5% country response rate) countries participated and 335(53.5% program response rate) surveys were initiated. There was 1 CR spot for every66 incident ischemic heart disease patients in LMICs. CR was most often paid by patients in LMICs (n=212,65.0%). On average, programs offered 7.31.8/11 core components over 33.730.7 sessions (significantly greater in publicly-funded programs;p<.001). Lack of patient referral (3.8/5) and financial resources (3.5/5) were the greatest barriers to CR provision in LMICs. CR is only available in 40% of LMICs, but where offered is fairly consistent with CR guidelines. Governments must enact policies to reimburse CR so patients do not pay out-of-pocket.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".