Association between junk food consumption and fast-food outlet access near school among Quebec secondary-school children: findings from the Quebec Health Survey of High School Students (QHSHSS) 2010–11
Bibliographic record
Abstract
OBJECTIVE: We investigated the association between junk food consumption at lunchtime (JCL) and fast-food outlet access near school among secondary-school children in Quebec. DESIGN: A geographic information system database was used to characterize the food environment around a sub-sample of 374 public schools in which 26 655 students were enrolled. The outcome variable was JCL during the previous week, dichotomized into low JCL (none or once) v. high JCL (twice or more). Access to fast-food outlets near school was assessed using an existing database of fast-food outlets in Quebec. Covariates included student (age, sex and self-rated perceived health), family (familial status and parental education) and school (urban/rural status and deprivation) variables. Hierarchical logistic regression models were employed for analyses using PROC GLIMMIX of SAS version 9.3. SETTING: Province of Quebec, Canada. SUBJECTS: We used data from the Quebec Health Survey of High School Students (QHSHSS) 2010-11, a survey of secondary-school Quebec students. RESULTS: Exposure to two or more fast-food outlets within a radius of 750 m around schools was associated with a higher likelihood of excess JCL (OR=1·50; 95 % CI 1·28, 1·75), controlling for the characteristics of the students, their families and their schools. CONCLUSIONS: The food environment surrounding schools can constitute a target for interventions to improve food choices among secondary-school children living in the province of Quebec. Transforming environments around schools to promote healthy eating includes modifying zoning regulations that restrict access to fast-food outlets around schools.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".