Cardiac rehabilitation program: An exploration of patient experiences and perspectives on program dropout
Bibliographic record
Abstract
BACKGROUND: Cardiac rehabilitation programs (CRP) are effective evidence-based secondary prevention programs that reduce morbidity and mortality in patients with cardiovascular disease (CVD). However, participation remains suboptimal, resulting in under-treatment and greater risk for recurrent cardiac events. Understanding the reasons behind CRP dropout is urgently needed to inform the development of programs that best meet patient needs and support sustained engagement. AIMS: The aim of this study was to identify and understand factors impacting CRP dropout from the patient perspective. METHODS: A qualitative study using semi-structured interviews was undertaken to examine the experience of 23 patients who dropped out of a CRP within a large urban hospital in British Columbia, Canada. Data were coded, analyzed using the constant comparison technique, and organized thematically. RESULTS: Participants described multiple challenges when attempting to complete CRP. Analysis of the data led to the identification of three main categories: (1) challenges living with CVD, (2) perceived advantages and disadvantages of CRP, and (3) unmet needs during CRP. LINKING EVIDENCE TO ACTION: In the practice setting, assessment of readiness to engage in CRP, alongside patient preferences and engagement needs, should be undertaken for maximum CRP uptake and completion. Providing diverse modes of CRP delivery, along with exploring the impact of virtual options as compared to traditional in-person programs, will further advance the CRP evidence and may help address pervasive access barriers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".