Obesity as a Determinant of Two Forms of Bullying in Ontario Youth: A Short Report
Bibliographic record
Abstract
OBJECTIVE: Obesity can have negative effects in terms of stigma and discriminatory behavior. Past cross-sectional analyses have shown that overweight and obese youths are more likely to be involved in bullying. Here, we examine such relationships in a longitudinal analysis. Study outcomes were self-reports of: i) physical bullying victimization and perpetration and ii) relational bullying victimization and perpetration. METHODS: Participants were administered the Health Behaviour in School-Age Children Survey in 2006 and then again in 2007, and included 1,738 youths from 17 Ontario high schools. Relationships between adiposity and each of the four forms of bullying were evaluated using multi-level analyses. RESULTS: Excess adiposity was shown to precede bullying involvement in this study. Obese and overweight males reported 2-fold increases in both physical and relational victimization, while obese females reported 3-fold increases in perpetration of relational bullying. Among those free of bullying at baseline (2006), significant increases in perpetration of relational bullying were reported by obese females in 2007 relative to normal-weight females (14.8 vs. 3.8% among normal-weight girls; p = 0.02). CONCLUSIONS: Findings are congruent with previous cross-sectional studies and confirm that obese youths are at increased risk of social consequences attributable to their appearance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".