Problem-solving differences in exercise behaviour motivation in cardiac rehabilitation participants
Bibliographic record
Abstract
Despite the positive outcomes of regular physical activity achieved in structured cardiac rehabilitation (CR), adherence to self-managed exercise post-CR is a problem. The Model of Social Problem-Solving (MSPS) suggests that individual differences in problem-solving abilities are linked to outcomes when individuals are challenged. Types of motivation are also linked to regulation of behaviour. Self-Determination Theory suggests the most positive outcomes are achieved through self-determined motivation (Vallerand et al., 2008). Self-determined regulation is related to increased persistence and likelihood to maintain behaviour (Deci & Ryan, 2002). Problem-solving is related to CR patients' exercise, but has not been examined relative to types of motivation. We compared CR participants more and less effective in problem-solving relative to (1) autonomous and (2) controlled motivation for regular exercise and (3) autonomy for CR exercise participation. Novice CR participants (N = 90; 66% male; Mage = 64.4 years) enrolled in a health region CR program completed measures of problem-solving effectiveness, autonomous and controlled motivation, autonomy, and persistence. MANOVA revealed a between-groups effect, Wilk's lambda = .74, p < .001, eta2 = .26. Follow-up ANOVAs revealed more effective problem-solvers had significantly higher autonomous motivation and autonomy, and lower controlled motivation (p's < .05, effect sizes = .06 to .19). Autonomous motivation and autonomy significantly predicted post-CR persistence with exercise (p's ≤ .05). Findings expand the emerging literature on problem-solving in CR patients' exercise and support MSPS-based contentions.Acknowledgments: Royal University Hospital Foundation Research Fund; SSHRC Canada Research Chair Training Funds
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".