Barriers and facilitators to virtual education in cardiac rehabilitation: a systematic review of qualitative studies
Bibliographic record
Abstract
BACKGROUND: Due to restrictions imposed by the severe acute respiratory syndrome coronavirus 2 pandemic much attention has been given to virtual education in cardiac rehabilitation (CR). Despite growing evidence that virtual education is effective in teaching patients how to better self-manage their conditions, there is very limited evidence on barriers and facilitators of CR patients in the virtual world. AIMS: To identify barriers and facilitators to virtual education participation and learning in CR. METHODS: A systematic review of peer-reviewed literature was conducted. Medline, Embase, Emcare, CINAHL, PubMed, and APA PsycInfo were searched from inception through April 2021. Following the PRISMA checklist, only qualitative studies were considered. Theoretical domains framework (TDF) was used to guide thematic analysis. The Critical Appraisal Skills Program was used to assess the quality of the studies. RESULTS: Out of 6662 initial citations, 12 qualitative studies were included (58% 'high' quality). A total of five major barriers and facilitators were identified under the determinants of TDF. The most common facilitator was accessibility, followed by empowerment, technology, and social support. Format of the delivered material was the most common barrier. Technology and social support also emerged as barriers. CONCLUSION: This is the first systematic review, to our knowledge, to provide a synthesis of qualitative studies that identify barriers and facilitators to virtual education in CR. Cardiac rehabilitation patients face multiple barriers to virtual education participation and learning. While 12 qualitative studies were found, future research should aim to identify these aspects in low-income countries, as well as during the pandemic, and methods of overcoming the barriers described.
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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 | 0.068 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".