Self-compassion and the self-regulation of exercise: Reactions to failures
Bibliographic record
Abstract
Most Canadians are not active enough to achieve the health benefits available through regular exercise. Low exercise adherence may be due to the self-regulatory effort required to maintain this behaviour. Self-compassion, treating oneself kindly in the face of failures, is recognized for its adaptive influence on self-regulation of health behaviours, including exercise. However, no research has examined self-compassion and its influence on individuals' response to exercise failures. This online, cross-sectional study's purpose was to investigate the role of self-compassion in the adaptive self-regulation of exercise goals in the context of a recalled exercise failure. Participants were 105 adults aged 18-64 years (M=42.94, SD = 17.18), who could recall a past exercise failure within the last six months. Participants completed online measures of self-compassion, self-esteem, demographics and then provided a detailed description of a past exercise failure. After, they completed questionnaires to assess adaptive self-regulation in this context. Semi-partial correlations revealed that after controlling for self-esteem, self-compassion was negatively related to external motivation (r = -0.207, p < 0.05), and state rumination (r = -0.391, p < 0.005) after an exercise failure. Hierarchical multiple regressions revealed that after controlling for age and self-esteem, self-compassion predicted unique variance in exercise goal re-engagement (r2 change = 0.07, F square change (1,101) = 7.6, p < 0.05) and negative affect (r2 change = 0.04, F square change (1,101) = 7.75, p < 0.05) after an exercise failure/set-back. Findings suggest that self-compassion may assist with the adaptive self-regulation of exercise after an exercise failure.Acknowledgments: Manitoba Graduate Student Scholarship
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".