Distance and Transportation as Barriers to Cardiac Rehabilitation in Urban and Rural Coronary Artery disease Outpatients
Bibliographic record
Abstract
Background: Cardiac rehabilitation (CR) is a proven means to reduce morbidity and mortality among cardiac outpatients but is grossly under-utilized. Transportation, distance and travel time are frequently cited barriers to participation. The purpose of this study was to compare CR participation rates between urban and rural cardiac outpatients and examine perceived distance and transportation barriers. Methods: 255 cardiac outpatients (mean age 68+11 years; 76%(194) male) of 97 Ontario cardiologists completed a survey within an on-going prospective study. The second digit of A0A in the postal code designated rural status and was verified with Statistics Canada 2001 Census. Using a 5-point Likert scale outpatients indicated the degree to which transportation and distance were barriers and self-reported travel time to CR and percentage of sessions attended. Results: 87% (223) of outpatients lived in an urban area, while 13% (32) were rural. Overall, 44%(113) participated in CR, with 46% (102) urban and 34% (11) being rural (P > 0.05). Transportation barriers were significantly related to CR participation (P < 0.01), whereas distance was not. Data were split by geographic area and transportation was only significantly related to CR participation among urban outpatients (P < 0.01). Urban outpatients reported a mean travel time of 25±18 minutes compared to 68±53 for rural outpatients (P < 0.0001). The mean percentage of CR sessions participated in was 84±28%, which did not differ by geographic status. Conclusions: Contrary to previous research, living in a rural area and perceived distance were not related to CR participation. However data collection is ongoing. Rural outpatients had longer travel times yet perceived no distance or transportation barriers. Transportation barriers for urban outpatients may be related to population density and traffic delays. Efforts to reduce transportation-related barriers in urban areas such as improving public transportation or increasing home-based CR provision may be 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.
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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".