Body Weight Perceptions Among Youth From 6 Countries and Associations With Social Media Use: Findings From the International Food Policy Study
Bibliographic record
Abstract
BACKGROUND: Body dissatisfaction is associated with poor psychological and physical health, particularly among young people. However, limited data exist on body size perceptions across countries and factors associated with dissatisfaction. OBJECTIVE: This study examined dissatisfaction prevalence and associations with sociodemographics and social media use among youth in 6 countries. DESIGN: Repeat cross-sectional national online surveys were conducted as part of the 2019 and 2020 International Food Policy Study Youth Survey. PARTICIPANTS: The sample included 21 277 youth aged 10 to 17 years from Australia, Canada, Chile, Mexico, the United Kingdom, and the United States. Youth were recruited to complete the online survey through parents/guardians enrolled in the Nielsen Consumer Insights Global Panel and their partners' panels. MAIN OUTCOME MEASURES: Figural drawing scales assessed self-perceived and ideal body images, with differences between scales representing body dissatisfaction. STATISTICAL ANALYSES PERFORMED: Multinomial logistic regression models examined differences in body dissatisfaction by country, and associations with sociodemographics and either social media screen time or platforms used, including 2-way interactions with country. RESULTS: Overall, approximately 45% of youth reported the same perceived and ideal body sizes, whereas 35% were "larger than ideal" (from 33% in Canada and Australia to 42% in Chile) and 20% were "thinner than ideal" (from 15% in Chile to 22% in Mexico). Greater social media screen time was associated with a higher likelihood of moderate-severe dissatisfaction for being "thinner than ideal" and at least mild dissatisfaction for being "larger than ideal" (P < .003 for all contrasts), with greater dissatisfaction among users of YouTube and Snapchat than nonusers (P ≤ .005 for both contrasts). Modest differences in body dissatisfaction between countries were observed for age, ethnicity, body mass index, and weight-based teasing. CONCLUSIONS: Body dissatisfaction is prevalent among youth across diverse countries. These findings highlight the need to promote healthy body image in youth, particularly among social media users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".