Identification of the Appropriate Boundary Size to Use When Measuring the Food Retail Environment Surrounding Schools
Bibliographic record
Abstract
This study included 6,971 students in grades 9 and 10 (ages 13 to 16 years) from 158 schools who participated in the 2009/2010 Health Behaviour in School-aged Children Study. Students provided information on where they typically ate lunch. The number of food retailers was obtained for six road network buffer sizes (500, 750, 1,000, 1,500, 2,000, and 5,000 meters) surrounding schools. Associations between the presence of food retailers near schools and students' lunchtime eating behaviours were examined using multilevel logistic regression. Comparisons of model fit statistics indicated that the 1,000 m buffer provided the best fit. At this distance, students with ≥3 food retailers near their schools had a 3.42 times greater relative odds (95% CI: 2.12-5.52) of eating their lunchtime meal at a food retailer compared to students with no nearby food retailers. Students who had ≥2 food retailers within 750 m of their schools had a 2.74 times greater relative odds (95% CI: 1.75-4.29), while those who had ≥1 food retailer within 500 m of their schools had 2.27 times greater relative odds of eating at food retailer (95% CI: 1.46-3.52) compared to those with no nearby food retailers. For distances greater than 1,000 m, no consistent relationships were found.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".