Identifying Elements for a Cardiac Rehabilitation Program for Caregivers: An International Delphi Consensus
Bibliographic record
Abstract
Background/Objectives: Caregivers of patients with heart disease may often feel physically, emotionally, and psychologically overwhelmed by their role. The analysis of cardiac rehabilitation (CR) components and caregivers’ needs suggests that some interventions may benefit them. Therefore, this study aimed to identify a consensus on the CR components targeting caregivers of patients with heart disease. Methods: A three-round international e-Delphi study with experts on CR was conducted. In round 1, experts provided an electronic level of agreement on a set of initial recommendations originating from a previous scoping review. In round 2, experts were asked to re-rate the same items after feedback and summary data were provided from round 1. In round 3, the same experts were asked to re-rate items that did not reach a consensus from round 2. Results: A total of 57 experts were contacted via e-mail to participate in the Delphi panel, and 43 participated. The final version presents seven recommendations for caregivers of patients with heart disease in CR programs. Conclusions: These recommendations are an overview of the evidence and represent a tool for professionals to adapt to their context in the different stages of CR, integrating the caregiver as a care focus and as support for their sick family members. By identifying the components/interventions, there is potential to benchmark the development of a cardiac rehabilitation strategy to be used and tested by the healthcare team for optimizing the health and role of these caregivers.
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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 | 0.230 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| 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".