Psychosocial components of cardiac recovery and rehabilitation attendance
Bibliographic record
Abstract
OBJECTIVE: To examine the relations between demographic factors, specific psychosocial factors, and cardiac rehabilitation attendance. DESIGN: Cohort, repeated measures design. SETTING: A large tertiary care centre in western Canada PATIENTS: 304 consecutive consenting patients discharged following acute myocardial infarction and/or coronary artery bypass graft surgery. MAIN OUTCOME MEASURES: The Jenkins self-efficacy expectation scales and activity checklists of behaviour performance for maintaining health and role resumption, modified version of the self-motivation inventory, and the shortened social support scale. RESULTS: Those who had higher role resumption behaviour performance scores at two weeks after discharge were significantly less likely to attend cardiac rehabilitation programmes. At six months after discharge, those who attended cardiac rehabilitation demonstrated higher health maintenance self-efficacy expectation and behaviour performance scores. Health maintenance self-efficacy expectation and behaviour performance improved over time. Women reported less social support but showed greater improvement in health maintenance self-efficacy expectation. Changes in self-efficacy scores were unrelated to-but changes in health maintenance behaviour performance scores were strongly associated with-cardiac rehabilitation attendance. CONCLUSIONS: Cardiac patients and practitioners may have misconceptions about the mandate and potential benefits of rehabilitation programmes. Patients who resumed role related activities early and more completely apparently did not see the need to "rehabilitate" while those who attended cardiac rehabilitation programmes enhanced their secondary prevention behaviours.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".