School Food Environment, Food Consumption, and Indicators of Adiposity Among Students 7‐14 Years in Bogotá, Colombia
Bibliographic record
Abstract
BACKGROUND: In Colombia, the prevalence of overweight/obesity in children has increased by 26% in the past 5 years. School food environment may be an important contributor and offers opportunities for effective intervention. METHODS: We conducted a cross-sectional study among 7- to 14-year-old schoolchildren from 10 schools in Bogotá, Colombia. We examined the school food environment and the relationship of individual-level consumption, and physical activity with overweight/obesity, measured by body mass index and percentage body fat. RESULTS: Schools with a restaurant/store were characterized as having excessive unhealthy foods. In the sample of 714 schoolchildren, 17.5% were overweight and 10.5% were obese. In multivariate models, boys had significantly increased odds of being overweight/obese (odds ratio [OR]: 1.53; p = .01) as compared to girls. Schoolchildren who consumed a greater number of energy drinks (OR = 1.82; p = .04), and those who spent more than 3 hours per schoolday watching TV (OR = 1.53; p < .01) had increased odds of being overweight/obese. CONCLUSIONS: Approximately 1 in 4 schoolchildren were overweight/obese, with boys, energy drink consumers, and those with low physical activity having increased risk. School-based interventions focused on improving food options and providing health behavior education may be effective to reduce overweight among children in Bogotá and similar settings.
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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 | 0.000 |
| 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".