Development and impact of exercise self-efficacy types during and after cardiac rehabilitation.
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease (CVD) is the leading cause of death in the developed world. Cardiac rehabilitation (CR) is a comprehensive treatment program centered on structured exercise that has been demonstrated to achieve significant decreases in mortality and morbidity in cardiac patients, yet few patients adhere to exercise post-CR and so fail to maintain any health benefits accrued during rehabilitation. One reason for the lack of adherence might be that CR fails to address the challenges to adherence faced by patients when they no longer have the resources and structure of CR to support them. Self-efficacy (SE) is a robust predictor of behavioral persistence. This study therefore focuses on changes in different types of SE during CR and the relationship of SE to subsequent levels of physical activity. METHOD: A sample of 63 CR patients completed assessments of task, scheduling and coping SE at baseline and the end of CR, as well as self-reported exercise behavior at the end of CR and 1-month post-CR. RESULTS: Task SE (for performing elemental aspects of the behavior) was found to be most changed type of SE during CR and was strongly related to self-reported exercise at the end of CR. However, scheduling SE (for performing the behavior regularly) was most strongly related to self-reported exercise post-CR. CONCLUSIONS: These results are theoretically consistent and suggest that scheduling SE should be targeted during CR to improve post-CR exercise adherence.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".