The proportion of excessive fast-food consumption attributable to the neighbourhood food environment among youth living within 1 km of their school
Bibliographic record
Abstract
The study objective was to estimate the proportion of excessive fast-food consumption by youth that is attributable to living and attending school in a neighbourhood with a moderate or high density of fast-food restaurants. This was a cross-sectional study of 6099 Canadian youths (aged 11-15 years) from 255 school neighbourhoods. All participants lived within 1 km of their school. The density of chain fast-food restaurants within a 1-km circular buffer surrounding each school was determined using geographic information systems. Excessive fast-food consumption (≥2 times per week) was assessed by questionnaire. Multilevel logistic regression analysis was used to examine associations. The population attributable risk estimates of excessive fast-food consumption due to neighbourhood exposure to fast-food restaurants were determined based on the prevalence of exposure and the results from the logistic regression. Eight percent of participants were excessive fast-food consumers. After adjusting for sociodemographic factors (i.e., gender, race, and socioeconomic status), it was found that youths from neighbourhoods with a moderate (odds ratio (OR), 1.68; 95% confidence interval (CI), 1.11-2.54) or high (OR 1.70; 95% CI 1.12-2.56) density of chain fast-food restaurants were more likely to be excessive fast-food consumers than were youths from neighbourhoods with no chain fast-food restaurants. Approximately 31% of excessive consumption was attributable to living in neighbourhoods with a moderate or high density of fast-food restaurants. Thus, the fast-food retail environment within which youth live and go to school is an important contributor to their eating behaviours.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".