Don't stop the music: The effects of appearance-focused music lyrics on body image during exercise
Bibliographic record
Abstract
Viewing music videos emphasizing the thin ideal female body has been shown to have a negative impact on body image in young women, including increased body dissatisfaction, social comparisons and body size discrepancies. However, it is unclear whether the changes in body image outcomes are due to the highly objectified images of women representing the thin ideal, or the lyrics of the songs. This study aimed to explore the effects of music lyrics on body image during exercise in physically active female university students. A repeated measure design was used; participants participated in two sessions in which they were asked to walk or run for 30 minutes while listening to music. In one condition, the music lyrics mentioned appearance, objectified the female body, or referenced the thin ideal. In the other condition, the lyrics did not refer to appearance at all. Participants completed state measures of mood, body image, self-objectification and body appreciation prior to and following their walk/run. Results indicated a statistically significant time effect (all ps < 0.05) for all outcomes except self-objectification, with women reporting feeling more confident, physically attractive, appreciative of their body and happier and feeling less fat, anxious, depressed and angry from pre- to post-exercise following both conditions. This study highlights the positive effects exercise has on body image and mood outcomes and suggests that exercise may negate the possible negative effects of objectifying lyrics. Results from this study suggest that appearance-focused music lyrics may not be harmful to body image in exercise settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".