Self-Reported Total Screen Time and Viewing Modes Are Associated with Body Dissatisfaction, Disordered Eating, and Cosmetic Surgery Intentions among Young Adults
Bibliographic record
Abstract
While numerous studies have shown that media exposure is linked to body dissatisfaction and disordered eating behavior, limited research has examined these associations by screen-viewing mode. This study examined associations of total screen-time and screen-viewing modes with body dissatisfaction, disordered eating, and cosmetic surgery intention among young adults. Men (n = 3466) and women (n = 7300), aged 19 to 34 years, self-reported their screen-time on various TV viewing modes, and their body dissatisfaction, overeating, disordered weight control behaviors, and cosmetic surgery intentions. We fit linear, logistic, and multivariate models to examine cross-sectional associations between total screen-time and screen-viewing modes and body dissatisfaction, disordered eating, and cosmetic surgery intention. Handheld viewing was associated with body dissatisfaction for women only, and online viewing was associated with greater body dissatisfaction among both men (βˆ = 0.40; 95% CI, 0.15 to 0.65) and women (βˆ = 0.25; 95% CI, 0.10 to 0.40). Downloaded viewing was associated with higher odds of overeating behaviors among both men (OR = 1.24; 95% CI, 1.10 to 1.40) and women (OR = 1.21; 95% CI, 1.12 to 1.32), respectively. Although total screen time was associated with greater cosmetic surgery intention for both men (βˆ = 0.24; 95% CI, 0.09 to 0.39) and women (βˆ = 0.43; 95% CI, 0.28 to 0.58), sex differences were found for the viewing modes. Our results suggest that different viewing modes may be differently associated with men and women’s body image, disordered eating behavior, and cosmetic surgery intention. Future research should consider all modes of screen-viewing in our media environment.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".