Cardiac Rehabilitation Services in Low- and Middle-Income Countries
Bibliographic record
Abstract
BACKGROUND: Despite the decreasing rate of cardiovascular disease-related mortality in developed nations, low- and middle-income countries (LMICs) are experiencing an increase. Cardiac rehabilitation (CR) successfully addresses this burden; however, the availability and nature of CR service delivery in LMICs are not well known. OBJECTIVE: This scoping review examined the (1) presence and accessibility of CR services, (2) structure of CR services, and (3) effects of CR on patient outcomes in LMICs. METHODS: Search criteria consisted of (1) nations considered to be low- or middle-income according to World Bank criteria, (2) CR, defined as programs including exercise and education, and (3) adults with cardiovascular diseases. Literature was identified through searching (a) the MEDLINE and EMBASE electronic databases, (b) proceedings from international cardiac conferences, (c) the grey literature and (d) through consulting experts in the field. RESULTS: Thirty peer-reviewed publications were identified. Grey literature, including Web sites for individual CR programs, revealed that CR is available in 32 (22.1%) LMICs. The most comprehensive data on accessibility stem from Latin America and the Caribbean, where 56% of institutions with cardiac catheterization facilities offered CR. Literature showed that some programs offered exercise, dietary advice, education, and psychological support, to assist patients to resume work and other activities of daily living. Fifteen peer-reviewed studies reported on CR outcomes, most of which were positive. CONCLUSION: Although patients similarly benefit from CR, few programs are available in LMICs. Policies need to be implemented to increase provision of tailored CR models at the global and national level, with evaluation.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".