Coping versus mastery modeling intervention to enhance self-efficacy for exercise in patients with COPD
Bibliographic record
Abstract
The literature suggests strong self-efficacy beliefs can facilitate long-term physical activity and management of Chronic Obstructive Pulmonary Disease (COPD). The purpose of this study was to (i) test the effects of two vicarious experience interventions, coping versus mastery modeling, on self-efficacy in COPD patients performing a cardiopulmonary exercise test (CPET), and (ii) determine the type of self-efficacy most strongly related to physical activity in COPD patients. After a baseline assessment of self-efficacy (task, coping for exercise, coping for breathing, scheduling, and walking) and potential moderators, 120 COPD patients watched a mastery model or coping model CPET video, or received usual care verbal instructions. Then, self-efficacy was assessed, followed by a CPET, and another assessment of self-efficacy. Fitbits tracked participants' step count the week following contact. Repeated measures MANOVAs assessed the intervention effects and multiple regressions assessed the contribution of self-efficacy sub-types to step count. All self-efficacy sub-types improved in the mastery and coping conditions, although greater improvement of self-efficacy for coping with exercise barriers was observed in the coping condition. Self-efficacy did not improve in the control condition and no moderators were identified. Self-efficacy for coping with exercise barriers was the self-efficacy sub-type most strongly related to step count. This research suggests modeling is a useful intervention technique to enhance self-efficacy in COPD patients, although coping models may be more beneficial than mastery models for enhancing capability beliefs during complex tasks. Future interventions in COPD patients should target self-efficacy for coping with exercise barriers.Acknowledgments: Canadian Institutes of Health Research (CIHR); G. F. MacDonald Centre for Lung Health Staff
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".