Obesogenic neighbourhoods: the impact of neighbourhood restaurants and convenience stores on adolescents’ food consumption behaviours
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between the neighbourhood food environment and dietary intake among adolescents. DESIGN: 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. SETTING: Mid-sized Canadian city in Ontario, Canada. Participants Grade 7 and 8 students (n 810) at twenty-one elementary schools. RESULTS: 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). CONCLUSIONS: 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".