Weight-related shame and guilt predict exercise behaviour: A test of the compensatory health beliefs model
Bibliographic record
Abstract
The compensatory health beliefs model (CHBM) suggests that humans engage in compensatory health behaviours (i.e. exercise) to alleviate or neutralize negative affective states (i.e. guilt, shame) that result from failure to achieve health goals (i.e. weight management). However, engaging in exercise behaviours for compensatory reasons has been associated with maladaptive psychological outcomes that may impede long-term exercise engagement. The present study utilized tenets of the CHBM to (i) examine the acute guilt and shame after self-weighing in predicting compensatory intentions and behaviors to exercise, and (ii) to assess if the emotional effects of self-weighing on compensatory exercise outcomes vary as a function of perceived competence to manage weight. Women seeking weight management (N=52, Mage=57.4?8.9; MBMI=35.3?6.2 kg/m2) completed a 7-day protocol of self-weighing and reported weight-related emotions, and exercise intentions and behaviours. Multilevel models revealed that when women felt more shame related to their weight than usual, they experienced more intentions to exercise (AŸ=0.53, p
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".