Does the neighborhood food environment contribute to ethnic inequalities in fast-food intake? findings from the ORiEL study
Bibliographic record
Abstract
The neighborhood food environment may contribute to ethnic inequalities in diet. Using data from 1389 participants in the Olympic Regeneration in East London (UK) study we assessed whether ethnic inequalities in neighborhood availability of fast-food restaurants mediated and/or modified ethnic inequalities in fast-food intake in 13-15 year-old adolescents. We compared the proportion of high fast-food consumers across "White UK", "Black", and "South Asian" ethnic categories. We used Poisson regression with robust standard errors to assess direct and indirect effects (mediation analysis) and risk ratios of high fast-food intake by ethnic category and fast-food restaurant availability level (effect measure modification analysis). There were ethnic inequalities in high fast-food intake, with risk ratios in adolescents of Black and South Asian background of 1.53 (95% CI: 1.25, 1.87) and 1.71 (95% CI: 1.41, 2.07) respectively compared to White UK participants. We found no evidence of a mediating effect by fast-food restaurant availability, but found some evidence of effect measure modification: ethnic inequalities in fast-food intake were largest in neighborhoods lacking fast-food restaurants, and narrowed as availability increased. Future research should explore why ethnic minorities are more likely to be high fast-food consumers than the majority ethnic group, especially when fast-food restaurant availability is lowest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".