Body-related self-conscious emotions and reasons for exercise: A latent class analysis
Bibliographic record
Abstract
Body-related self-conscious emotions are important predictors of exercise motivation, yet the association between body-related self-conscious emotions and reasons for exercise has not been explored. Researchers have typically examined body-related emotions (e.g., shame, guilt, pride, embarrassment, envy) in isolation, but they may interact in unique ways to predict reasons for exercise. The present study examined how patterns of body-related emotions were associated with exercise reasons. In an online survey, participants ( N = 520; M age = 35.43 ± 10.09; 57.5 % men) reported their experience of body-related self-conscious emotions and exercise reasons over the past week. Latent class analysis revealed a three-class model of emotions, resulting in a High Emotionality class (i.e., experiencing positive and negative emotions), a Negative Emotions class, and a Pride class. Individuals who experienced negative emotions about their bodies engaged in exercise for appearance reasons, while individuals who felt proud about their bodies and did not report the negatively valenced emotions reported exercising for health reasons. These findings underscore the importance of investigating how multiple body-related self-conscious emotions influence reasons for exercising. Understanding how patterns of body-related self-conscious emotions are experienced could inform future research on factors that may precede exercise motivation and increase exercise behavior.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".