Development and Evaluation of the International Council of Cardiovascular Prevention and Rehabilitation (ICCPR) Program Certification for Low-Resource Settings
Bibliographic record
Abstract
Background: Cardiac rehabilitation (CR) is a proven model of secondary prevention, but new sites, providing quality care, are needed in low-resource settings. This study (1) described the development of International Council of Cardiovascular Prevention and Rehabilitation's (ICCPR) Program Certification and (2a) tested its implementation, considering (b) appropriateness of quality standards for these settings. Methods: The Steering Committee finalized 13 standards, requiring 70% be met. They are assessed initially through International CR Registry (ICRR) program survey and patient data; if Certification appears possible, a two-hour virtual site assessment is arranged to corroborate. Standard operating procedures for Assessor training were developed. A multi-method pilot study was then undertaken with a quantitative (description of quality indicators) and qualitative (focus groups on MS Teams) component. ICRR sites with post-program data by April 2022 were invited to participate. Two team members independently analyzed focus group transcripts, using a deductive-thematic approach with NVIVO. Results: Five CR programs from the Eastern Mediterranean, South-East Asian and American regions participated. Upon application, with some data cleaning, initially four programs were eligible to proceed to virtual site assessment. Ultimately, all five programs were certified, each meeting a minimum of 12/13 standards (peak MET increase and program completion rate were not met by some centres). Four themes resulted from the two focus groups of 13 site data stewards: motivation and benefits (eg, international recognition, additional program resources), logistics (eg, communication, cost, site visit process), the standards and their assessment (eg, balance of rigor and feasibility), and suggestions for improvement (eg, website). Conclusion: ICCPR's Program Certification has been demonstrated to be feasible, rigorous, and acceptable. Standards are attainable in low-resource settings. Certified programs reap benefits including additional resources. This first international Certification is suitable for low-resource settings, to complement that from the American and European CR Societies.
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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 | 0.109 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".