Developmental associations between victimization and body mass index from 3 to 10 years in a population sample
Bibliographic record
Abstract
In the current prospective study, we investigated (1) whether high and low BMI in early childhood puts a child at risk of victimization by their peers, and (2) whether being victimized increases BMI over the short- and long-term, independent of the effect of BMI on victimization. We also examined whether gender moderated these prospective associations. Participants were 1,344 children who were assessed yearly from ages 3 to 10 years as part of the Québec Longitudinal Study of Child Development (QLSCD). BMI predicted annual increases in victimization for girls aged 6 years and over; for boys aged 7 and 8 years of age, higher BMI reduced victimization over the school year. Further, victimization predicted annual increases in BMI for girls after age 6 years. When these short-term effects were held constant, victimization was also shown to have a three and 5-year influence on annual BMI changes for girls from age 3 years. These short- and long-term cross-lagged effects were evident when the effects of family adversity were controlled. The findings support those from previous prospective research showing a link between higher BMI and victimization, but only for girls. Further, being victimized increased the likelihood that girls would put on weight over time, which then increased future victimization. The implications of these prospective findings for interventions are considered. Aggr. Behav. 42:109-122, 2015. © 2015 Wiley Periodicals, Inc.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".