Associations between muscularity-oriented social media content and muscle dysmorphia among boys and men
Bibliographic record
Abstract
This study aimed to determine whether viewing muscularity-oriented social media content was associated with muscle dysmorphia among a sample of boys and men from Canada and the United States. Data from the Study of Boys and Men (2024; N = 1553) were analyzed. Multiple linear regression analyses were conducted to determine the associations between viewing content with 1) muscular bodies, 2) muscle-building dietary supplements (e.g., whey protein), and 3) muscle-building drugs (e.g., anabolic-androgenic steroids) on social media and probable muscle dysmorphia. Findings revealed strong and positive associations between viewing muscularity-oriented social media content and probable muscle dysmorphia. Specifically, greater frequency of viewing content related to muscular bodies, muscle-building dietary supplements, and muscle-building drugs were all associated with having probable muscle dysmorphia, independent of total time spent on social media. The findings from this study underscore the need for more research to understand the directionality and risks associated with specific social media content among boys and men. Greater media and health literacy is needed for boys and men to support appropriate social media use. • Viewing muscular bodies on social media is associated with muscle dysmorphia. • Viewing muscle-building supplements on social media is linked with muscle dysmorphia. • Viewing muscle-building drugs on social media is linked with muscle dysmorphia. • Resources are needed to support appropriate social media engagement in boys and men.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".