Factors Associated With Attendance at a 1-yr Post–Cardiac Rehabilitation Risk Factor Check
Bibliographic record
Abstract
PURPOSE: Patients with coronary artery disease (CAD) often fail to maintain secondary prevention gains after completing cardiac rehabilitation (CR). Follow-up appointments aimed at assessing cardiac status and encouraging maintenance of health behaviors after CR completion are generally offered but not well-attended. This study explored patient characteristics and barriers associated with nonattendance at a 1-yr follow-up visit following CR completion. METHODS: Forty-five patients with CAD who completed a 12-wk outpatient CR program but did not attend the 1-yr follow-up appointment were included. Participants responded to a survey consisting of open-ended questions about follow-up attendance, a modified version of the Cardiac Rehabilitation Barriers Scale, and self-report items regarding current health practices and perceived strength of recommendation to attend. Thematic analysis was used to derive categories from open-ended questionnaire responses. Linear regression was used to assess characteristics associated with appointment attendance barriers. RESULTS: Barrier themes were as follows: (1) lack of awareness; (2) perception of appointment as unnecessary; (3) practical or scheduling issues; (4) comorbid health issues; and (5) anticipated an unpleasant experience at the appointment. Greater self-reported barriers (mean ± SD = 1.97/5.00 ± 0.57) were significantly associated with lower perceived strength of recommendation to attend the follow-up appointment (2.82/5.00 ± 1.45), P = .005. CONCLUSIONS: Providing a stronger recommendation to attend, enhancing patient awareness, highlighting potential benefits, and supporting self-efficacy might increase 1-yr follow-up appointment attendance and, in turn, support long-term adherence to cardiovascular risk reduction behaviors.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".