Promoting cardiac rehabilitation program quality in low-resource settings: Needs assessment and evaluation of the International Council of Cardiovascular Prevention and Rehabilitation's registry quality improvement supports
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation (CR) registries have the potential to support quality improvement (QImp). This study investigated the QImp needs of International CR Registry-participating programs and their evaluation of its' supports. METHODS: ICRR offers comparative outcome dashboards and QImp sessions, among other features. In this qualitative study, ICRR data stewards from the 17 active on-boarded CR programs were invited to a focus group held in November 2023 via Teams; stewards not sufficiently-proficient in English were invited to provide written input. Deductive-thematic analysis using NVIVO was undertaken by 2 researchers; member-checking ensued. RESULTS: Nine participated, and four provided input, from eight countries. Three themes emerged; saturation was achieved. First, QImp facilitators included training, institutional requirements, dedicated staff, resources in academic centres and ICRR features. Second, QImp barriers included staffing issues, the global nature of the ICRR, and structural challenges in low-resource settings. Finally, ICRR supports for QImp included didactic webinars, hearing from other programs, 1-1 support offered and assessing minimum Certification standards. CONCLUSION: ICRR-participating programs are satisfied with QImp supports but encounter challenges, including related to language, staffing and other resources. CR registries should be leveraged and optimized to support CR programs to assess and improve their care quality.
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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 | 0.173 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".