Physician Factors Affecting Cardiac Rehabilitation Referral and Patient Enrollment: A Systematic Review
Bibliographic record
Abstract
Physicians play an important role in CR referral and enrollment. Despite established benefits and recommendations, cardiac rehabilitation (CR) enrollment rates are pervasively low. The reasons cardiac patients are missing from CR programs are multifactorial and include provider factors. A number of studies have now investigated physician factors associated with referral to CR programs and patient enrollment. The objective of this study was to qualitatively and systematically review this literature. A literature search of MEDLINE, PsycINFO, CINAHL, Embase, and EBM was conducted for published articles from database inception to October 2011. Overall, 17 articles were included following a process of independent review of each article by 2 authors. Seven (41.2%) were graded as good quality according to Downs and Black criteria. There were no randomized controlled trials. Results showed that medical specialty (ie, cardiac specialists more likely to refer; n = 8 studies) and other physician-reported reasons (eg, physician report of their reasons for CR referral and physician sex) were related to referral. Physician factors related to patient enrollment in CR were physician endorsement, medical specialty, being referred, and physician attitudes toward CR. Physician factors are consistently related to CR referral and enrollment. The role of physician endorsements in promoting patient enrollment should be optimized and exploited.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".