Screen time, social media use, and weight-related bullying victimization: Findings from an international sample of adolescents
Bibliographic record
Abstract
Screen time, social media, and weight-related bullying are ubiquitous among adolescents. However, little research has been conducted among international samples. Therefore, the aim of this study was to determine the association between screen time, social media use, and weight-related bullying victimization among an international sample of adolescents from six countries. Data from the 2020 International Food Policy Study Youth Survey were analyzed (N = 12,031). Multiple modified Poisson regression models were estimated to determine the associations between weekday hours of five forms of screen time, and total screen time, and use of six contemporary social media platforms and weight-related bullying victimization. Analyses were conducted among the overall sample, and stratified by country (Australia, Canada, Chile, Mexico, United Kingdom, United States). Greater hours of weekday screen time and use of each of the six social media platforms were associated with weight-related bullying victimization among the sample. Each additional hour of social media use was equivalent to a 13% (confidence interval [CI] 1.10-1.16) increase in the prevalence of weight-related bullying victimization. The use of Twitter was associated with a 69% (CI 1.53-1.84) increase in the prevalence of weight-related bullying victimization. Associations between hours of weekday screen time, use of six social media, and weight-related bullying victimization differed by country. Findings underscore the associations between screen time, social media, and weight-related bullying among a sample of adolescents from six medium- and high-income countries. Country-specific and global public health and technology efforts are needed to address this burgeoning social problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".