Relationships Between Violent Sexual Victimization and Muscle‐Building Exercise among Adolescents from the 2019 Youth Risk Behavior Survey
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the association between violent sexual victimization and muscle-building exercise among adolescents. METHODS: Cross-sectional data from the 2019 National Youth Risk Behavior Survey (N = 8408) were analyzed. Two indicators of non-dating-related sexual violence (lifetime, past 12 months), along with one indicator of adolescent sexual violence (past 12 months), were assessed. Days of muscle-building in the past week were reported. Multinomial logistic regression analyses were conducted to determine the associations between the three violent sexual victimization variables and muscle-building exercise. RESULTS: Among the sample (50.3% girls), lifetime violent sexual victimization (relative risk ratio [RRR] 1.87, 95% confidence interval [CI] 1.29 to 2.72), past 12-month violent sexual victimization (RRR 1.60, 95% CI 1.15 to 2.22), and past 12-month sexual dating violence (RRR 1.60, 95% CI 1.03 to 2.51) were associated greater relative risk of high engagement (ie, 6 to 7 days) in muscle-building exercise relative to no engagement. CONCLUSIONS: A history of violent sexual victimization is associated with greater involvement in muscle-building exercise, particularly among adolescent girls. Engagement in muscle-building exercise in relation to violent sexual victimization may be a means of emotion regulation and occur due to body dissatisfaction resulting from experiencing violence.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".