A comparative analysis of the restaurant consumer food environment in Rochester (NY, USA) and London (ON, Canada): assessing children’s menus by neighbourhood socio-economic characteristics
Bibliographic record
Abstract
OBJECTIVE: To assess restaurant children's menus for content and nutritional quality; and to investigate the relationship between the restaurant consumer food environment for children and neighbourhood-level socio-economic characteristics within and between one Canadian city and one US city. DESIGN: Cross-sectional observational study. SETTING: London, ON, Canada and Rochester, NY, USA.ParticipantsRestaurant children's menus were assessed, scored and compared using the Children's Menu Assessment tool. We quantified neighbourhood accessibility to restaurants by calculating 800 m road-network buffers around the centroid of each city census block and created a new Neighbourhood Restaurant Quality Index for Children (NRQI-C) comprising the sum of restaurant menu scores divided by the total number of restaurants within each area. After weighting by population, we examined associations between NRQI-C and neighbourhood socio-economic characteristics using correlations and multiple regression analyses. RESULTS: Nutritional quality of children's menus was greater, on average, in Rochester compared with London. Only one variable remained significant in the regression analyses for both cities: proportion of visible minorities had a positive effect on neighbourhood NRQI-C scores in London, whereas the reverse was true in Rochester. CONCLUSIONS: Results suggest the presence of a socio-economic disparity within Rochester, where children in more disadvantaged areas have poorer access to better nutritional quality restaurant choices. In London, results suggest an inverse relationship across the city where children in more disadvantaged areas have better access to better nutritional quality restaurant choices. Given these disparate results, research on restaurant nutritional quality for children requires additional consideration.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".