Cardiac rehabilitation delivery in low/middle-income countries
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it