Cardiac rehabilitation delivery model for low-resource settings
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease is a global epidemic, which is largely preventable. Cardiac rehabilitation (CR) is demonstrated to be cost-effective and efficacious in high-income countries. CR could represent an important approach to mitigate the epidemic of cardiovascular disease in lower-resource settings. The purpose of this consensus statement was to review low-cost approaches to delivering the core components of CR, to propose a testable model of CR which could feasibly be delivered in middle-income countries. METHODS: A literature review regarding delivery of each core CR component, namely: (1) lifestyle risk factor management (ie, physical activity, diet, tobacco and mental health), (2) medical risk factor management (eg, lipid control, blood pressure control), (3) education for self-management and (4) return to work, in low-resource settings was undertaken. Recommendations were developed based on identified articles, using a modified GRADE approach where evidence in a low-resource setting was available, or consensus where evidence was not. RESULTS: Available data on cost of CR delivery in low-resource settings suggests it is not feasible to deliver CR in low-resource settings as is delivered in high-resource ones. Strategies which can be implemented to deliver all of the core CR components in low-resource settings were summarised in practice recommendations, and approaches to patient assessment proffered. It is suggested that CR be adapted by delivery by non-physician healthcare workers, in non-clinical settings. CONCLUSIONS: Advocacy to achieve political commitment for broad delivery of adapted CR services in low-resource settings is needed.
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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 | 0.062 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".