Barriers and Facilitators to Delivering Inpatient Cardiac Rehabilitation: A Scoping Review
Bibliographic record
Abstract
Marina Wasilewski,1– 3 Abirami Vijayakumar,1 Zara Szigeti,1 Sahana Sathakaran,2 Kuan-Wen Wang,2 Adam Saporta,1 Sander L Hitzig1 1St. John’s Rehab, Sunnybrook Research Institute, North York, Ontario, Canada; 2Department of Occupational Science & Occupational Therapy, University of Toronto, Toronto, Ontario, Canada; 3Rehabilitation Sciences Institute (RSI), University of Toronto, Toronto, Ontario, CanadaCorrespondence: Marina Wasilewski, St. John’s Rehab, Sunnybrook Research Institute, North York, Ontario, Canada, Email marina.wasilewski@sunnybrook.caObjective: The purpose of this scoping review was to summarize the literature on barriers and facilitators that influence the provision and uptake of inpatient cardiac rehabilitation (ICR).Methods: A literature search was conducted using PsycINFO, MEDLINE, EMBASE, CINAHL and AgeLine. Studies were included if they were published in English after the year 2000 and focused on adults who were receiving some form of ICR (eg, exercise counselling and training, education for heart-healthy living). For studies meeting inclusion criteria, descriptive data on authors, year, study design, and intervention type were extracted.Results: The literature search resulted in a total of 44,331 publications, of which 229 studies met inclusion criteria. ICR programs vary drastically and often focus on promoting physical exercises and patient education. Barriers and facilitators were categorized through patient, provider and system level factors. Individual characteristics and provider knowledge and efficacy were categorized as both barriers and facilitators to ICR delivery and uptake. Team functioning, lack of resources, program coordination, and inconsistencies in evaluation acted as key barriers to ICR delivery and uptake. Key facilitators that influence ICR implementation and engagement include accreditation and professional associations and patient and family-centred practices.Conclusion: ICR programs can be highly effective at improving health outcomes for those living with CVDs. Our review identified several patient, provider, and system-level considerations that act as barriers and facilitators to ICR delivery and uptake. Future research should explore how to encourage health promotion knowledge amongst ICR staff and patients.Keywords: inpatient, cardiovascular diseases, cardiac rehabilitation, early mobilization, scoping review
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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 | 0.036 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 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".