A Social-Ecological Perspective of the Perceived Barriers and Facilitators to Virtual Education in Cardiac Rehabilitation
Bibliographic record
Abstract
PURPOSE: This study explored the perceived barriers and facilitators to participation in patients who did and did not attend virtual cardiac rehabilitation (CR) education sessions. METHODS: A mixed-methods approach was used. Virtual patient education was delivered during the coronavirus-19 pandemic. Phase 1 included a cross-sectional online survey completed by individuals who did and did not participate in these sessions. For phase 2, six virtual focus group sessions were conducted using the social-ecological framework to guide thematic analysis and interpretation of findings. RESULTS: Overall, 106 online surveys were completed; 60 (57%) attended Cardiac College Learn Online (CCLO) sessions only, one (1%) Women with Heart Online (WwHO) only, 21 (20%) attended both, and 24 (22%) did not attend virtual sessions. Half of the participants who attended virtual sessions viewed between one and four sessions. Most participants were from Canada (95%) and included the Toronto Rehabilitation Institute/Toronto Western Hospital centers (76%). Focus group findings revealed six overarching themes: Intrapersonal (mixed emotions/feelings; personal learning preferences); Interpersonal (desire for warmth of human contact and interaction); Institutional (the importance of external endorsement of sessions); and Environmental (technology; perceived facilitators and barriers). CONCLUSION: These findings highlight the unprecedented situation that patients and CR programs are facing during the pandemic. Virtual patient education may be more accessible, convenient, and responsive to the complex needs of these CR participants.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".