Cardiac rehabilitation barriers among under-represented groups, and the role of targeted program model allocation
Bibliographic record
Abstract
Purpose: Despite the well-established benefits of cardiac rehabilitation (CR) and greater need, under-represented populations are less likely to utilize CR compared to their counterparts. To date, there has been limited research to quantify CR barriers in these under-represented groups and there has been lack of research to assess whether barriers differ by program model. This dissertation examined CR utilization and barriers to CR use among rural and urban inhabitants, patients of low socioeconomic status (SES) and high SES, Chinese-Canadian and North American patients, and home-based versus site-based CR. \n\nMethod: Cardiac patients from hospitals across Ontario, Canada completed a survey which included the Cardiac Rehabilitation Barriers Scale among other variables for this cross-sectional study. \n\nResults: Findings suggested that rural inhabitants attended significantly fewer CR sessions, and perceived greater CR barriers overall compared to urban inhabitants. These included distance, cost, and transportation problems. In addition, patients of lower SES were less likely to be referred, enroll, and participate in CR, and reported significantly greater barriers to CR compared to their high-SES counterparts. Greater barriers for low-SES patients included severe weather, distance, cost, and transportation problems. Moreover, Chinese-Canadian patients were significantly more likely to be referred to CR compared to North Americans, but there were no significant differences with regard to utilization. Chinese-Canadian patients reported significantly greater CR barriers compared to North Americans, specifically severe weather and transportation problems. Also, appropriately, home-based CR participants reported greater barriers including distance when compared to site-based participants. \n\nConclusion: Broader application of proven strategies to promote greater CR enrolment and completion is needed, as well as development of tailored interventions to address the primary barriers identified for these vulnerable subpopulations of patients.
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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 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".