The food environment surrounding Vancouver schools : associations of access to food outlets and children's intake of minimally nutritious foods at or en-route to school
Bibliographic record
Abstract
Background: Canada has seen a dramatic increase in the prevalence of childhood obesity in recent decades. Researchers have argued that this problem could be addressed through improvements to the “food environment”—the food vendors comprised in the built environment. Children's diets are poorer in nutritional quality during school hours, suggesting that the food environments surrounding schools may be an important area of inquiry. Objectives: This thesis sought (1) to identify the best available data set for assessing the distributions of food outlets in Vancouver, (2) to characterize the food environments surrounding Vancouver public schools, testing for demographic or socioeconomic disparities in access and (3) to examine the associations between school food environments and the dietary intakes of children and adolescents at- or en-route to school. Methods: Food outlet data were obtained from two municipal and two commercial sources and validated against primary data on the food outlets located within 800m of 26 schools. Outlet density and proximity to Vancouver schools (n=113) were evaluated with the best performing data set; negative binomial regression models examined whether disparities existed in environments according to % aboriginal students, % English Language Learners, and school poverty, controlling for neighbourhood-level factors. Multilevel logistic regression analyses evaluated the associations of school food environment measures and 950 children's odds of daily consumption, at or en-route to schools (n=26), of minimally nutritious foods. Results & Conclusions: The City of Vancouver Business Licenses data had the highest sensitivity (0.69) and positive predictive value (0.55). High-poverty schools had more convenience stores within 400m than low-poverty schools, even after controlling for commercial density and neighbourhood socioeconomic deprivation (IRR=1.74, 95% CI 1.003 - 3.032); no robust statistically significant relationships were identified between school food environments and school-level demographic factors. No consistent associations were identified between school food environment measures and students' intakes of minimally nutritious foods. The findings do not support policies to reduce student access to food outlets near schools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".