COMPARING THE WILLINGNESS, BARRIERS, AND KNOWLEDGE OF CARDIAC REHABILITATION BETWEEN ELDERLY AND NON-ELDERLY CARDIOVASCULAR PATIENTS IN AREAS WITH LIMITED RESOURCES
Bibliographic record
Abstract
Objective: This study aimed to investigate the differences in CR willingness, participation barriers, and CR knowledge between elderly and non-elderly cardiovascular patients in areas with limited resources. Design and method: A cross-sectional study was conducted among hospitalized cardiovascular patients in the cardiology department of a comprehensive hospital in Shanghai, China. A total of 414 valid questionnaires were collected. The survey included demographic and clinical information, CR willingness, the Chinese/Mandarin Cardiac Rehabilitation Barriers Scale (CRBS-C/M) to assess CR barriers, and the Coronary Artery Disease Education Questionnaire Short Version (CADE-Q SV) to evaluate CR knowledge. Data analysis included descriptive statistics, chi-square tests, t-tests, and binary logistic regression to identify differences between elderly and non-elderly patients and determine factors influencing CR willingness. Results: 43.2% of patients expressed willingness to participate in CR if recommended by a doctor. Elderly patients experienced fewer time conflicts but faced greater barriers related to program and health system factors, as well as comorbidities/functional status. No significant differences were found between elderly and non-elderly patients regarding CR willingness and CR knowledge. Factors influencing CR willingness included perceived CR need obstacles (OR = 0.740, p < 0.001, 95% CI = 0.648–0.846), family history of cardiovascular disease (yes vs. no: OR = 2.683, p < 0.001, 95% CI = 1.535–4.690), time conflict (OR = 0.897, p = 0.017, 95% CI = 0.820–0.981), and comorbidities/functional status barriers (OR = 0.902, p = 0.025, 95% CI = 0.824–0.987). Conclusions: Elderly patients have similar CR willingness and knowledge as non-elderly patients, but face greater barriers in program and health system factors as well as comorbidities/functional status. This study highlights the necessity of implementing targeted interventions and public health strategies for elderly cardiovascular disease patients, particularly in resource-limited areas, to improve awareness and accessibility of CR, which is crucial for better cardiovascular outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".