Automatic Referral to Cardiac Rehabilitation
Bibliographic record
Abstract
OBJECTIVES: Cardiac rehabilitation (CR) remains underused and inconsistently accessed, particularly for women and minorities. This study examined the factors associated with CR enrollment within the context of an automatic referral system through a retrospective chart review plus survey. Through the Behavioral Model of Health Services Utilization, it was postulated that enabling and perceived need factors, but not predisposing factors, would significantly predict patient enrollment. SUBJECTS: A random sample of all atherosclerotic heart disease (AHD) patients treated at a tertiary care center (Trillium Health Centre, Ontario, Canada) from April 2001 to May 2002 (n = 501) were mailed a survey using a modified Dillman method (71% response rate). MEASURES: Predisposing measures consisted of sociodemographics such as age, sex, ethnocultural background, work status, level of education, and income. Enabling factors consisted of barriers and facilitators to CR attendance, exercise benefits and barriers (EBBS), and social support (MOS). Perceived need factors consisted of illness perceptions (IPQ) and body mass index. RESULTS: Of the 272 participants, 199 (73.2%) attended a CR assessment. Lower denial/minimization, fewer logistical barriers to CR (eg, distance, cost), and lower perceptions of AHD as cyclical or episodic reliably predicted CR enrollment among cardiac patients who were automatically referred. CONCLUSION: Because none of the predisposing factors were significant in the final model, this suggests that factors associated with CR enrollment within the context of an automatic referral model relate to enabling factors and perceived need. A prospective controlled evaluation of automatic referral is warranted.
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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 | 0.012 |
| 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) | 0.008 | 0.001 |
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".