Factors Affecting Cardiac Rehabilitation Referral by Physician Specialty
Bibliographic record
Abstract
PURPOSE: Cardiac rehabilitation (CR) is widely underutilized because of multiple factors including physician referral practices. Previous research has shown CR referral varies by type of provider, with cardiologists more likely to refer than primary care physicians. The objective of this study was to compare factors affecting CR referral in primary care physicians versus cardiac specialists. METHODS: A cross-sectional survey of a stratified random sample of 510 primary care physicians and cardiac specialists (cardiologists or cardiovascular surgeons) in Ontario identified through the Canadian Medical Directory Online was administered. One hundred four primary care physicians and 81 cardiac specialists responded to the 26-item investigator-generated survey examining medical, demographic, attitudinal, and health system factors affecting CR referral. RESULTS: Primary care physicians were more likely to endorse lack of familiarity with CR site locations (P < .001), lack of standardized referral forms (P < .001), inconvenience (P = .04), program quality (P = .004), and lack of discharge communication from CR (P = .001) as factors negatively impacting CR referral practices than cardiac specialists. Cardiac specialists were significantly more likely to perceive that their colleagues and department would regularly refer patients to CR than primary care physicians (P < .001). CONCLUSIONS: Where differences emerged, primary care physicians were more likely to perceive factors that would impede CR referral, some of which are modifiable. Marketing CR site locations, provision of standardized referral forms, and ensuring discharge summaries are communicated to primary care physicians may improve their willingness to refer to CR.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".