Reasons for non-adherence to cardiac rehabilitation programmes included lack of motivation, domestic duties, and other health problemsCommentary
Bibliographic record
Abstract
S Greenfield Dr S Greenfield, University of Birmingham, Birmingham, UK; s.m.greenfield@bham.ac.uk Why do post-myocardial infarction (MI) or revascularisation patients not adhere to home-based or hospital-based cardiac rehabilitation programmes (CRPs)? Semi-structured interviews. 4 hospitals and participant’s homes in the UK. Purposive sample of 49 patients (age range 34–87 y, 67% men) who had MI or revascularisation and did not adhere to home-based (n = 21) or hospital-based (n = 28) CRPs were identified from a randomised controlled trial. The home-based CRP included a copy of the Heart Manual (6-wk exercise and walking programme), information tapes, home visits, and telephone calls from nurses. The hospital-based CRP included group or individual exercise based on circuit training, and combined or separate sessions of education and relaxation. At 3–20 months after randomisation, participants were individually interviewed for 40–45 minutes about their cardiac event, expectations and experience in CRPs, and lifestyle changes. Interviews were tape recorded, transcribed, and analysed for themes and subthemes. In general, reasons for non-adherence to CRPs were multifactorial …
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".