SOCIODEMOGRAPHIC VARIATIONS IN EXPOSURE TO FAST FOOD RESTAURANTS AND ITS ASSOCIATION WITH FAST FOOD CONSUMPTION AMONG YOUTH
Bibliographic record
Abstract
Introduction Canadians eat at fast food restaurants more regularly than in the past. Fast foods are higher in energy, saturated fats, and sodium than most other foods, and the portion sizes are often very large. Thus, excessive fast food consumption contributes to an unhealthy diet and a host of adverse health effects. Individuals with a low socioeconomic status (SES) and ethnic minorities are at increased risk for excessive fast food consumption. Another determinant of fast food consumption is the density of fast food restaurants in the environment. There is a greater density of fast food restaurants in poorer neighbourhoods and neighbourhoods with larger ethnic minority populations. Disparities may also exist in the extent to which fast food restaurants are associated with fast food consumption. However, this relationship has not been examined in young Canadians. Objectives To examine the associations between fast food restaurant density in the home neighbourhood and fast food consumption within youth, and to determine whether this association is modified by SES and ethnicity. Methods Data are from the 2009/2010 Canadian Health Behaviour in School-Aged Children survey, which is a nationally representative sample of 26,078 grade 6–10 students. Frequency of fast food consumption, SES, ethnicity, home postal code, and several confounders were assessed by self-report. Density of chain fast food restaurants within 1 km of each participant's home postal code was determined using computerized geographic information systems (GIS). A multilevel logistic regression model will be used to determine the relationship between neighbourhood fast food restaurant density and excessive fast food consumption (>2 times/week). Regression models will test for SES and ethnicity interactions and will control for several confounding variables. Results and conclusion To be presented at conference (work in progress).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".