Fitness-related self-conscious emotions and sport motivation in adolescent females: Does perceived athletic competence moderate the effect
Bibliographic record
Abstract
Sport participation can provide many benefits to adolescent athletes; however, female sport participation tends to decrease significantly during adolescence (Eime et al., 2016; Sabiston et al., 2012). A commonly cited reason for this dropout is issues surrounding body image, such as fitness-related body self-conscious emotions (FSCE; pride, shame, guilt, envy and embarrassment; Castonguay et al., 2016). Researchers have suggested that perceived athletic competence (PAC) may buffer the negative relationship between SCE and sport outcomes (Sabiston & Pila, 2016). Thus, this study examined the relationship between FSCE, PAC, and sport motivation among adolescent female athletes. A sample of female athletes (13 to 17 years old) completed measures of FSCE, PAC, and sport motivation. Linear correlations indicated that fitness-related embarrassment (r= -.47), envy (r= -.46), shame (r= -.50), guilt (r= -.35), authentic pride (r=.47) and hubristic pride (r=.35), as well as PAC (r=.34), were all correlated with sport motivation (Sport Motivation Scale II: RAI composite score). All FSCE scales were moderately to highly inter-correlated. Due to multicollinearity, only shame and PAC were entered as individual predictors. Regression analysis showed that shame (B= -.45), but not PAC (B= .10) was a significant predictor of RAI (R squared= .29). There was also no evidence for PAC acting as a buffer as the moderated regression analysis indicated that a PAC x Shame interaction term did not make a significant additional contribution beyond main effects (R squared delta= .001). This research provides further evidence of the important contribution of body self-conscious emotions in predicting sport motivation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".