Drive time to cardiac rehabilitation: at what point does it affect utilization?
Bibliographic record
Abstract
BACKGROUND: A 30 minute drive time threshold has often been cited as indicative of accessible health services. Cardiac rehabilitation (CR) is a chronic disease management program designed to enhance and maintain cardiovascular health, and geographic barriers to utilization are often cited. The purpose of this study was to empirically test the drive time threshold for CR utilization. METHODS: A prospective study, using a multi-level design of coronary artery disease outpatients nested within 97 cardiologists. Participants completed a baseline sociodemographic survey, and reported CR referral, enrollment and participation in a second survey 9 months later. CR utilization was verified with CR sites. Geographic information systems were used to generate drive times at 60, 80 and 100% of the speed limit to the closest CR site from participants' homes, to take into consideration various traffic conditions. Bivariate analysis was used to test for differences in CR referral, enrollment and degree of participation by drive time. Logistic regression was used to test drive time increments where significant differences were found. RESULTS: Drive times were generated for 1209 outpatients. Overall, CR referral was verified for 523 (43.3%) outpatients, with verified enrollment for 444 (36.7%) participating in a mean of 86.4 +/- 25.7% of prescribed sessions. There were significant differences in CR referral and enrollment by drive time (ps < .01), but not degree of participation. Logistic regression analysis (ps < .001) revealed that the drive time threshold at 80% of the posted speed limit for physician referral may be 60 minutes (OR = .26, 95% CI: 0.13-0.55), and the threshold for patient CR enrollment may also be 60 minutes (OR = .11, 95% CI: 0.04-0.33). CONCLUSIONS: Physicians may be taking geography into consideration when referring patients to CR. Empirical consideration also reveals that patients are significantly less likely to enroll in CR where they must drive 60 minutes or more to the closest program. Once enrolled, distance has no significant effect on degree of participation.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".