Awareness of Canada’s Food Guide Among Canadian Youth
Bibliographic record
Abstract
Purpose: Canada’s Food Guide (CFG) contains recommendations for healthy eating for Canadians. The objective was to examine the awareness of and learning about CFG by Canadian youth. Methods: Cross-sectional online surveys were conducted with 3,674 youth aged 10–17 years in Canada in November/December 2019. Logistic binary regression models examined awareness of CFG, learning about CFG in school, and learning about healthy eating in schools in the past 12 months. Results: Most participants reported hearing of CFG (84.5%), learning about CFG in school (86.6%), and learning about healthy eating in school (65.4%) in the past 12 months. Awareness of CFG was higher among females (OR: 1.61; 95% CI: 1.32–1.96), older youth (1.70; 1.39–2.07), and those in Atlantic Canada (OR: 1.77; 95% CI: 1.10, 2.84). Significantly fewer East/Southeast Asian, South Asian, Latino, and Middle Eastern participants reported hearing of CFG compared to white participants (p < 0.05 for all). Unstated/missing BMI (0.56; 0.45–0.71) and living in BC (OR: 0.61; 95% CI: 0.45, 0.82) were negatively associated with hearing about CFG. Similar results were observed in the models on learning about CFG and healthy eating in school. Conclusions: This study indicates discrepancies in awareness of CFG among youth by sex, ethnicity, region, and BMI which may suggest differences in use of CFG and healthy 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".