Understanding the role of guilt and shame in physical activity self-regulation
Bibliographic record
Abstract
Control theorists suggest that negative emotions result when goal progress is thwarted and, in turn, motivates goal pursuit. Control theorists do not differentiate between negative emotions or their implications for self-regulation yet self-conscious emotion researchers recognize distinctions between guilt and shame with different self-regulatory influences. Guilt results when transgressions are attributed to lack of effort and motivates effort. Attributed to inability, shame leads to goal disengagement. We examined guilt and shame relative to recent exercise behavior, as well as each emotion's motivational properties. In this online study, 175 adults completed measures of recent exercise quantity and quality, attributions, and shame and guilt relative to a day when they did and a day when they did not engage in intended exercise. Participants experienced more guilt (t = -10.784, p < .0001) and shame (t = -7.075, p < .0005) after a missed than an engaged-in exercise session. Of these two emotions guilt was felt more intensely (t = -6.613, p < .0001). Regressions determined that exercise quality was negatively related to both guilt (beta = -.429, p > .001) and shame (beta = -.499, p > .001); these emotions were not related to exercise intentions. Guilt was associated with an internal locus of casualty (beta = .393, p > .05) and shame with stability (beta = .248, p >.05). Logistic regressions showed that shame (beta = -.11, p = .05), not guilt, was (negatively) associated with exercise. Findings partially support, within an exercise context, propositions about shame and guilt in self-regulation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 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".