The characteristics of cardiac rehabilitation interventions provided by nurses: A scoping review
Bibliographic record
Abstract
Abstract Background: A cardiac rehabilitation program (CRP) is an interdisciplinary intervention that combines the medical, social, and educational components necessary for cardiac rehabilitation so that people with cardiovascular disease can regain their functional abilities and improve their quality of life. In CRP, nurses have an essential role in patient care and service coordination. The basic components of a CRP that can be adapted remain unknown, as there are many different CRP. This study aims to identify the key features of a CRP that can inform public policy. Currently, there is no synthesis of the characteristics of services in CRP that can be adapted in the literature. This study aims to identify the main characteristics of cardiac rehabilitation programs nurses offer when providing cardiac rehabilitation after a myocardial infarction. Methods: A scoping study was conducted according to Arksey and O'Malley (2005). We systematically searched CINAHL, MEDLINE, Scopus, and Cochrane. Only empirical studies published between 2005 and 2022 with a CRP involving a nurse were included. CRP characteristics were classified, coded, and validated with the research team and categorized for analysis. Results: Fifteen articles were included in which 15 rehabilitation programs were identified. In total, four programs were offered, individualized nurse follow-up (n = 8), CRP solicitation (n = 2), use of technology (n = 3), and telehealth (n = 1). Overall, the features were categorized into four program types: 1) Interventions promoting early recruitment of participants; 2) Interventions promoting technology use; 3) Interventions based on multiple components; and 4) Interventions based on surveillance and monitoring activities. Conclusion: With the increase in myocardial infarctions, more and more patients need CRP. Nurse monitoring during CRP can be offered with several components to facilitate follow-up. These different follow-up components allow the nurse to anticipate care issues and obstacles the patient may encounter throughout cardiac rehabilitation. These elements should be promoted for engagement and participation in CR to improve CR provision. Finally, the characteristics of this study will support the conceptualization of a CR program.
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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 | 0.020 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.032 | 0.037 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".