Weight Status and Behavioral Problems among Very Young Children in Chile
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Our objective was to explore the association between weight status and behavioral problems in children before school age. We examined whether the association between weight status and behavioral problems varied by age and sex. SUBJECTS/METHODS: This study used cross-sectional data from a nationally-representative sample of children and their families in Chile (N = 11,207). These children were selected using a cluster-stratified random sampling strategy. Data collection for this study took place in 2012 when the children were 1.5-6 years of age. We used multivariable analyses to examine the association between weight status and behavioral problems (assessed using the Child Behavior Checklist), while controlling for child's sex, indigenous status, birth weight, and months breastfed; primary caregiver's BMI and education level; and household wealth. RESULTS: Approximately 24% of our sample was overweight or obese. Overweight or obese girls showed more behavioral problems than normal weight girls at age 6 (β = 0.270 SD, 95% CI = 0.047, 0.493, P = 0.018). Among boys age 1 to 5 years, overweight/obesity was associated with a small reduction in internalizing behaviors (β = -0.09 SD, 95% CI = -0.163, -0.006, P = 0.034). CONCLUSIONS: Our data suggest that the associations between weight status and behavioral problems vary across age and sex.
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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 | 0.000 | 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) | 0.001 | 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".