Automatic Referral to Cardiac Rehabilitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it