Co-designing a cardiac rehabilitation program with knowledge users for patients with cardiovascular disease from a remote area
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease is the leading cause of death worldwide. Cardiac rehabilitation (CR) programs are recognized as effective in reducing the burden of cardiovascular disease. However, CR programs are offered inequitably across regions and are available in less than 15% of remote areas worldwide. The main goal of this study was to design a CR program adapted to the contexts of remote areas to improve the service offered to patients. METHODS: We used an iterative user-centered design approach to understand the user context and services offered in cardiac rehabilitation in remote areas. We conducted two co-design processes with knowledge users in two remote regions. Two advisory committees were created in each of these regions, comprising managers (n = 6), healthcare professionals (n = 12) and patients (n = 2). We utilized the BACPR guidelines and the Hautes Autorités de santé operational model to support data collection in coding sessions to develop the CR program. We conducted four cycles of co-design with each of the committees to develop the cardiac rehabilitation program. Qualitative data were analyzed iteratively after each cycle. RESULTS: The co-design process resulted in developing a prototype cardiac rehabilitation program similar in both regions. It is based on a contextualized six-phase pathway of care designed for remote regions. For each phase 0 to 6 of the care pathway, knowledge users were asked to describe how to offer these phases in remote areas. Participants made structural changes to phases 0, 2, 3 and 4 in order to overcome staffing shortages in remote areas. These changes make it possible to decentralize cardiac rehabilitation expertise away from specialized centers, to ensure equity of service across the territory. Therapeutic patient education was integrated into phase 4 to meet patients' needs. Participants suggested that three follow-up offerings could come from nursing services to increase access to the cardiac rehabilitation program (primary care, home care, special chronic disease programs) in patients' home communities. CONCLUSION: The co-design process enables us to meet the needs of remote regions in program development. This final program can be the subject of future implementation research.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".