Participating in cardiac rehabilitation: a systematic review and meta-synthesis of qualitative data
Bibliographic record
Abstract
BACKGROUND: Participation in cardiac rehabilitation (CR) benefits patients with coronary heart disease (CHD), yet worldwide only some 15–30% of those eligible attend. To improve understanding of the reasons for poor participation we undertook a systematic review and meta-synthesis of the qualitative literature. METHODS: Qualitative studies identifying patient barriers and enablers to attendance at CR were identified by searching multiple electronic databases, reference lists, relevant conference lists, grey literature, and keyword searching of the Internet (1990–2010). Studies were selected if they included patients with CHD and reviewed experience or understanding about CR. Meta-synthesis was used to review the papers and to synthesize the data. RESULTS: From 1165 papers, 34 unique studies were included after screening. These included 1213 patients from eight countries. Study methodology included interviews (n = 25), focus groups (n = 5), and mixed-methods (n = 4). Key reasons for not attending CR were physical barriers, such as lack of transport, or financial cost, and personal barriers, such as embarrassment about participation, or misunderstanding the reasons for onset of CHD or the purpose of CR. CONCLUSIONS: There is a vast amount of qualitative research which investigates patients’ reasons for non-attendance at CR. Key issues include system-level and patient-level barriers, which are potentially modifiable. Future research would best be directed at investigating strategies to overcome these 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 | 0.089 | 0.196 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".