Cardiac rehabilitation delivery in low/middle-income countries
Bibliographic record
Abstract
OBJECTIVE: Cardiac rehabilitation (CR) availability, programme characteristics and barriers are not well-known in low/middle-income countries (LMICs). In this study, they were compared with high-income countries (HICs) and by CR funding source. METHODS: A cross-sectional online survey was administered to CR programmes globally. Need for CR was computed using incident ischaemic heart disease (IHD) estimates from the Global Burden of Disease study. General linear mixed models were performed. RESULTS: CR was identified in 55/138 (39.9%) LMICs; 47/55 (85.5% country response rate) countries participated and 335 (53.5% programme response) surveys were initiated. There was one CR spot for every 66 IHD patients in LMICs (vs 3.4 in HICs). CR was most often paid by patients in LMICs (n=212, 65.0%) versus government in HICs (n=444, 60.2%; p<0.001). Over 85% of programmes accepted guideline-indicated patients. Cardiologists (n=266, 89.3%), nurses (n=234, 79.6%; vs 544, 91.7% in HICs, p=0.001) and physiotherapists (n=233, 78.7%) were the most common providers on CR teams (mean=5.8±2.8/programme). Programmes offered 7.3±1.8/10 core components (vs 7.9±1.7 in HICs, p<0.01) over 33.7±30.7 sessions (significantly greater in publicly funded programmes; p<0.001). Publicly funded programmes were more likely to have social workers and psychologists on staff, and to offer tobacco cessation and psychosocial counselling. CONCLUSION: CR is only available in 40% of LMICs, but where offered is fairly consistent with guidelines. Governments should 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".