Assessing Physician Barriers to Cardiac Rehabilitation Referral Rates in a Tertiary Teaching Centre
Bibliographic record
Abstract
Introduction: Cardiac rehabilitation (CR) has a proven morbidity and mortality benefit, yet rates of referral remain low. We sought to elucidate the knowledge, utilization, referral, and endorsement practices of cardiac rehabilitation in a tertiary care centre. Methods: A 13-question survey was electronically distributed to all Internal Medicine residents, Cardiology residents and subspecialty fellows, General Internal Medicine attendings and Cardiology attendings practising in a tertiary care centre. The survey assessed the physicians’ knowledge of what CR entails, its benefits, patient eligibility and personal practices with respect to CR referral. Results: The survey was distributed to 153 physicians with a response rate of nearly 60 percent. Compared to their medicine counterparts, Cardiology residents and staff had significantly improved knowledge with respect to what CR entails and eligibility criteria for referral (6.92 vs. 6.11 out of 9, p=0.036; 12.04 vs. 10.76 out of 17, p = 0.013). Medicine residents and staff were less likely to be familiar with CR guidelines (72.02 vs. 32.69, p<0.01), and were less likely to discuss the importance of CR attendance with their patients (43.28 vs. 71.15, p=0.0002). A higher proportion of those in Medicine also reported being unsure of both how to refer eligible patients (59.12 vs. 13.46, p<0.0001) and which patients were eligible for CR (64.92 vs. 23.08, p<0.0001). Higher knowledge scores and familiarity with CR guidelines was associated with higher self-reported referral rates. Conclusion: This survey has identified clear physician barriers, most significant among internal medicine residents and staff. These barriers can help inform interventions to improve CR referral and enrolment rates.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".