Self-compassion, affect, and health-promoting behaviors.
Bibliographic record
Abstract
OBJECTIVE: Emerging theory and research suggest that self-compassion promotes the practice of health behaviors, and implicates self-regulation as an explanatory factor. However, previous investigations focused only on behavior intentions or health risk behaviors, and did not investigate the role of emotions. This study expands on this research using a small-scale meta-analysis approach with our own data sets to examine the associations of self-compassion with a set of health-promoting behaviors, and test the roles of high positive affect and low negative affect as potential explanatory mechanisms. METHOD: Fifteen independent samples (N = 3,252) with correlations of self-compassion with the frequency of self-reported health-promoting behaviors (eating habits, exercise, sleep behaviors, and stress management) were meta-analyzed. Eight of these samples completed measures of positive and negative affect. RESULTS: Self-compassion was positively associated with the practice of health-promoting behaviors across all 15 samples. The meta-analysis revealed a small effect size (average r = .25; p < .001) of self-compassion and health behaviors, with low variability. Tests of the indirect effects of self-compassion on health behaviors through positive and negative affect with multiple mediator analyses revealed small effects for each. Separate meta-analyses of the indirect effects (IE) were significant for positive (average IE = .08; p < .001) and negative affect (average IE = .06; p < .001), and their combined indirect effects (average IE = .15; p < .0001). CONCLUSION: Self-compassion may be an important quality to cultivate for promoting positive health behaviors, due in part to its association with adaptive emotions.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".