Unhealthy food options in the school environment are associated with diet quality and body weights of elementary school children in Canada
Bibliographic record
Abstract
OBJECTIVE: Increasing evidence links unhealthy food environments with diet quality and overweight/obesity. Recent evidence has demonstrated that relative food environment measures outperform absolute measures. Few studies have examined the interplay between these two measures. We examined the separate and combined effects of the absolute and relative densities of unhealthy food outlets within 1600 m buffers around elementary schools on children's diet- and weight-related outcomes. DESIGN: This is a cross-sectional study of 812 children from thirty-nine schools. The Youth Healthy Eating Index (Y-HEI) and daily vegetables and fruit servings were derived from the Harvard Food Frequency Questionnaire for Children and Youth. Measured heights and weights determined BMI Z-scores. Food outlets were ranked as healthy, somewhat healthy and unhealthy according to provincial paediatric nutrition guidelines. Multilevel mixed-effects regression models were used to assess the effect of absolute (number) and relative (proportion) densities of unhealthy food outlets within 1600 m around schools on diet quality and weight status. SETTING: Two urban centres in the province of Alberta, Canada. PARTICIPANTS: Grade 5 students (10-11 years). RESULTS: For children attending schools with a higher absolute number (36+) of unhealthy food outlets within 1600 m, every 10 % increase in the proportion of unhealthy food outlets was associated with 4·1 lower Y-HEI score and 0·9 fewer daily vegetables and fruit. CONCLUSIONS: Children exposed to a higher relative density of unhealthy food outlets around a school had lower diet quality, specifically in areas where the absolute density of unhealthy food outlets was also high.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".