Women’s resistance training and body-related self-conscious emotions: an integrated theoretical perspective
Bibliographic record
Abstract
Physical activity is a promising strategy for improving women's body image. Engaging in resistance training (RT) may lead to unique body-related outcomes when compared to other forms of physical activity (e.g., aerobic activity) yet is less studied in women’s body image research. In an integrative model, the purpose of this study was to explore the relationships between women's RT, positive body image (functionality appreciation and body appreciation), physical self-perceptions (perceived strength and confidence in physical abilities), and body-related self-conscious emotions (appearance- and fitness-related shame, guilt, envy, embarrassment, authentic and hubristic pride). A sample of 400 women (Mage = 25.6 ± 5.3 years) completed an online survey. We used path analysis to test the direct effects of RT minutes/week on body-related self-conscious emotions, controlling for other forms of moderate-to-vigorous physical activity, and the indirect effects through positive body image and physical self-perceptions. In the models, RT had no direct effects on appearance-related emotions but was associated with lower fitness-related guilt and higher fitness-related pride. Indirectly, higher RT engagement was linked to lower appearance- and fitness-related shame, guilt, envy, and embarrassment, and higher pride through positive body image and physical self-perceptions. Together, the variables accounted for 23-58% of the variance in body-related self-conscious emotions. These findings underscore the need for further longitudinal and intervention studies to explore whether RT may serve as a viable pathway to improving women's positive body image, physical self-perceptions, and body-related self-conscious emotions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".