Perceptions of Cardiac Specialists and Rehabilitation Programs Regarding Patient Access to Cardiac Rehabilitation and Referral Strategies
Bibliographic record
Abstract
In Brief BACKGROUND: Access to cardiac rehabilitation (CR) remains at approximately 30%, despite a national target of 70%. This study evaluated cardiac specialist and CR program perceptions of CR access and referral strategies. METHODS: Postal and online surveys of Canadian CR specialists and CR programs were administered. Responses were received from 71 of 765 CR specialists (9.3%) and 92 of 149 CR programs (61.7%). Respondents rated perceptions on 5-point Likert scales. RESULTS: Specialists rated patient access to CR as moderate (2.9 ± 1.4). While they reported that they refer 65.9% of their patients, they most frequently do not refer because their patients report disinterest (23.4%) or geographic barriers to access (23.4%). Cardiac rehabilitation programs reported having capacity to serve a median of 275 patients annually, yet reportedly serving up to 350. The most commonly used methods of referral included discharge order sets (over 60%) and allied health care provider support. Electronic referral was perceived to be highly effective (4.1 ± 1.0) yet the least frequently used. Cardiac rehabilitation programs perceived more patients are accessing CR because of these referral strategies, but increased patients strain program resources. CONCLUSIONS: Some of the least frequently used referral strategies were perceived as, and are also empirically demonstrated to be, most effective. Broader implementation of these strategies, while better-resourcing CR programs, may improve the continuum of care for cardiac patients. This national assessment of cardiac rehabilitation (CR) access from the vantage point of specialists and programs revealed that access might be limited by infrequent application of effective referral strategies, patient report of disinterest or geographic barriers to physicians leading to referral failure, and lack of CR program funding and capacity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".