A cross-sectional study of cardiac rehabilitation enrollment barriers in patients at risk for suboptimal outcomes from acute coronary syndrome
Bibliographic record
Abstract
Purpose: Cardiac rehabilitation (CR) is an effective treatment to reduce the burden of cardiovascular disease (CVD) but is underutilized. This study characterized CR enrollment barriers and perceived physician endorsement of CR in patient subgroups at increased risk of poor outcomes.Materials and Methods: The association between sociodemographic and clinical characteristics and Cardiac Rehabilitation Barriers Scale (CRBS) item and subscale scores were examined using secondary data analysis of patients with acute coronary syndrome referred to, but not yet enrolled in, a 12-week CR program. Participants rated perceived strength of recommendation to attend CR on 1–5 scale.Results: The three most endorsed CRBS items were inclement weather, travel, and work responsibilities. Additional barriers (e.g. time constraints, already exercising, family responsibilities) emerged in certain patient subgroups. Perceived strength of physician endorsement was high in the overall sample. After statistical adjustment for confounds, depressed mood was positively associated with logistical (b = 0.05, p = 0.002), and comorbidity-related barriers (b = 0.02, p < 0.001). Female sex (b = 0.62, p = 0.004), higher body mass index (b = 0.05, p = 0.009), and diabetes (b = 1.08, p < 0.001), were associated with logistical barriers.Conclusions: Patients require individualized support to address CR enrollment barriers. Given their crucial role in supporting patients to access CR, nurses are well-positioned to identify and address CR barriers.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".