Explaining the self-regulatory role of affect in identity theory: The importance of self-compassion
Bibliographic record
Abstract
People who identify with exercise verify their identity through exercise engagement and experience negative affect if they fail to behave in accordance with their identity. Identity theory posits that negative affect should motivate identity-consistent exercise. This link is not established, and other behaviour change theories suggest negative affect can thwart goal pursuits. Self-compassion (SC) is the tendency to relate to oneself with support and is associated with tolerance of negative emotions. SC may moderate the relationship between any negative emotions exercisers experience when they behave inconsistently with their exercise identity and both their exercise intensions and perceptions of exercise identity-behaviour consistency. We examined this possibility in a week-long, online, prospective study where 274 exercisers who had recently failed to engage in sufficient exercise to verify their identity completed measures of demographics, SC, negative emotions (state shame and guilt) and indicated their exercise intentions for the following week. One week later, exercisers reported the extent to which their past week’s exercise aligned with their identity standard on a scale of 0% to 100% (i.e., identity-behaviour consistency). SC moderated the relationship between state guilt and identity-behaviour consistency (p < .029) such that guilt was positively associated with identity-behaviour consistency for those high in SC but negatively related to identity behaviour consistency among those low in SC. No other moderated relationships were significant. People who are self-compassionate may cope better with feelings of guilt, and instead of becoming paralyzed by guilt, use this emotion as a force to motivate behaviour change.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".