Factors Associated With Non-enrollment of Center-Based Cardiovascular Rehabilitation Program Among Transient Ischemic Attack or Mild Stroke Patients
Bibliographic record
Abstract
PURPOSE: Cardiovascular rehabilitation programs (CRPs) are effective in secondary stroke prevention, yet the enrollment rate is suboptimal. This study aims to identify demographic and clinical factors and patient-reported reasons for non-enrollment in a center-based outpatient CRP among patients with transient ischemic attack (TIA) or mild stroke. METHODS: This mixed-method retrospective chart review was conducted in an outpatient CRP affiliated with a tertiary care hospital in Canada from January 2009 to October 2017. A total of 621 patients with TIA or mild stroke were included. Multiple logistic regression was used to determine the relationship between demographic and clinical predictors with non-enrollment. A thematic analysis of multidisciplinary progress notes was done for the non-enrollment subgroup of patients to understand the patient-reported reasons. RESULTS: The non-enrollment rate was 42%. Travel distance to CRP (OR = 1.024; 95% CI, 1.010-1.038), age (OR = 1.023; 95% CI, 1.004-1.042), and current smoking status (OR = 1.935; 95% CI, 1.230-3.042) were associated with non-enrollment. The patient-reported reasons for non-enrollment were occurrence of new medical events and comorbidities, their perceptions of health and CRP, transportation, work/time conflict, and distance. CONCLUSIONS: This study found that patients with TIA or mild stroke who were older, lived farther from the CRP center, or were current smokers were less likely to enroll in a CRP. The present findings may help clinicians identify patients unlikely to enroll in a CRP and allow the implementation of interventions focused on health education and physical activity to improve enrollment. Future research should validate these factors in multiple settings using prospective mixed methods so that interventions can be developed to address non-enrollment in the CRP.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".