Obesogenic Neighborhoods: The Impact of Neighborhood Restaurants and Convenience Stores on Adolescents' Food Consumption Behaviors
Bibliographic record
Abstract
To examine the relationship between the neighbourhood food environment and dietary intake among adolescents. Cross-sectional design using: (i) a geographic information system to assess characteristics of the neighbourhood food environment and neighbourhood socio-economic status; (ii) the modified Healthy Eating Index (HEI) to assess participants' overall diet quality; and (iii) generalized linear models to examine associations between HEI and home and school food environmental correlates. Mid-sized Canadian city in Ontario, Canada. Participants Grade 7 and 8 students (n 810) at twenty-one elementary schools. Students living in neighbourhoods with a lower diversity of land-use types, compared with their higher diversity counterparts, had higher HEI scores (P < 0.05). Students with more than 1 km between their home and the nearest convenience store had higher HEI scores than those living within 1 km (P < 0.01). Students attending schools with a distance further than 1 km from the nearest convenience store (P < 0.01) and fast-food outlet (P < 0.05) had higher HEI scores than those within 1 km. Those attending schools with three or more fast-food outlets within 1 km had lower HEI scores than those attending schools with no fast-food outlet in the school surroundings (P < 0.05). Close proximity to convenience stores in adolescents' home environments is associated with low HEI scores. Within adolescents' school environments, close proximity to convenience and fast-food outlets and a high density of fast-food outlets are associated with low HEI scores.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".