Patterns of motivation and ongoing exercise activity in cardiac rehabilitation settings: A 24-month exploration from the teach study
Bibliographic record
Abstract
Few studies have explored exercise and motivational patterns of cardiac rehabilitation patients in the long term. The first purpose of this study was to explore if differential patterns of exercise exists in cardiac rehabilitation patients over a 24-month period. A second aim was to examine the trajectories of motivational constructs and their relationship with emerging patterns of exercise. Cardiac rehabilitation patients (n=251) completed an exercise, barrier self-efficacy, outcome expectations and self-determined motivation questionnaire over a 2-year period. Latent class growth modeling was used to classify patients in different exercise and motivational patterns. Three exercise patterns emerged: inactive, non-maintainers and maintainers (16%; 67% and 17% of sample per pattern, respectively). Multiple trajectories were found for barrier self-efficacy, outcome expectations, and self-determined motivation (3, 5, and 4, respectively). Analyses showed that patients in high barrier self-efficacy, outcome expectation, and self-determined groups had greater probability of being in the maintainer exercise group. This study demonstrated that cardiac rehabilitation participants vary significantly in maintaining exercise patterns over a 24-month period. Identifying a patient's motivational profile could help cardiac rehabilitation programs tailor their intervention to optimize the potential for continued exercise activity.Acknowledgments: This study was supported by a research grant from the Heart and Stroke Foundation of Ontario (HBR 4600). Shane N. Sweet was supported by a doctoral fellowship from the Social Science and Humanities Research Council of Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".