Physician management preferences for cardiac patients: factors affecting referral to cardiac rehabilitation.
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation (CR) is an evidence-based intervention that has been shown to reduce both morbidity and mortality. However, CR is widely underused due to multiple factors, including physician referral practices. OBJECTIVES: To describe physicians' preferences in managing cardiac patients and the barriers they face in referring patients to CR. METHODS: A cross-sectional survey of a stratified random sample of 510 primary care physicians, cardiologists and cardiovascular surgeons in Ontario was conducted. One hundred seventy-nine physicians responded (40% response rate through repeat mailings) to the survey that investigated medical, demographic and attitudinal factors affecting referral. A hypothetical case scenario that elicited open-ended factors affecting physician management preferences was incorporated. RESULTS: Physicians identified geographic access, uncertainty regarding which provider was responsible for referral and perceptions of patient motivation as important factors affecting referral to CR. Through principal components analysis, several attitudes affecting referral emerged, including beliefs about the efficacy of CR, referral norms, ease of the referral process and desire to manage the patient independently. A hierarchical logistic regression analysis showed that 75% of the variance in referral was attributable to medical specialty, availability of CR and practice norms. CONCLUSIONS: Increased communication among health care providers is needed to ensure CR referral. Due to geographic dispersion, alternatives to site-based CR are necessary to meet the needs of cardiac patients.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".