Do schools in Quebec foster healthy eating? An overview of associations between school food environment and socio-economic characteristics
Bibliographic record
Abstract
OBJECTIVE: A school environment that encourages students to opt for food with sound nutritional value is both essential and formative in ensuring that young people adopt healthy eating habits. The present study explored the associations between the socio-economic characteristics of the school environment and the school food environment. DESIGN: A cross-sectional survey was conducted in 2008-2009. Descriptive and bivariate analyses were performed on data from public primary and secondary schools. SETTING: Quebec, Canada. The school food offering was observed directly and systematically by trained research assistants. Interviews were conducted to fully describe food offerings in the schools and schools' child-care services. SUBJECTS: A two-stage stratified sampling was used to build a representative sample of 143 French-speaking public schools. The response rate was 66.2%. RESULTS: The primary and secondary schools in low-density areas were more likely to be located near diners (primary: P=0.018; secondary: P=0.007). The secondary schools in deprived areas were less likely to have a regular food committee (P=0.004), to seek student input on menu choices (P=0.001) or to have a long lunch period (P=0.010). The primary schools in deprived areas were less likely to have a food service (P=0.025) and their meal periods were shorter (P=0.033). CONCLUSIONS: The schools in areas with lower socio-economic status provided an environment less favourable for a healthy diet. From a public health perspective, the results of this analysis could assist policy makers and managers to identify actions to support the creation of favourable school environments.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".