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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".