Losing, gaining, or staying the same: how do different weight change attempts relate to muscle dysmorphia and eating disorder symptoms across genders?
Bibliographic record
Abstract
Adolescents and young adults are at heightened risk for eating disorder (ED) and muscle dysmorphia (MD) symptoms; yet, these symptoms and their relationships to harmful behaviors may also vary by gender. Thus, this study examined: 1) the prevalence of attempts to lose, gain, or maintain the same weight across gender identities, 2) purposes of weight change attempts, and 3) relationships between weight change attempts and ED and MD symptoms across cisgender men, women, and transgender and gender expansive (TGE) youth. 940 adolescents and young adults (57.4% cisgender women, 33.8% cisgender men, 8.8% TGE) completed questionnaires about weight change attempts, ED and MD symptoms. Women and TGE individuals attempted to lose weight more often than men, while men attempted to gain weight more often. All genders endorsed weight loss and gain attempts for different purposes. Weight loss attempts related to ED symptoms and appearance intolerance, whereas weight gain attempts related to MD symptoms across genders. In women, all weight change attempts related to greater functional impairment due to exercise. Findings highlight the need for tailored interventions to address desires to change one's body and underscore the harmful effects of weight change attempts across genders.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".