Social Determinants of Adolescent Eating Behaviours: Findings from the 2019 Canadian Health Survey on Children and Youth
Bibliographic record
Abstract
Understanding how eating behaviours differ across sociodemographic groups is crucial for identifying at-risk youth and informing targeted public health strategies. This study examined how these behaviours vary by sociodemographic characteristics in a nationwide sample of the 2019 Canadian Health Survey on Children and Youth (n=13,605 youth aged 12-17 years). Covariate-adjusted logistic regression models were used to test for differences in four eating behaviours (frequency of breakfast consumption, participation in evening family meals, consumption of sugary drinks, and restrictive eating) by gender, age, racial/cultural background, parental education, household income, and food security status. Girls were more likely to skip breakfast and change their eating habits to manage their weight, but less likely to report frequent sugary drink consumption and frequent family meals compared to boys. Older youth demonstrated less favourable practices when compared with younger youth. Differences in eating behaviours were observed among racial subgroups, with Black youth being more at risk of reporting sub-optimal eating behaviours compared to White youth. Adolescents with lower parental education were more likely to report infrequent breakfast and regular sugary beverage consumption. Food insecurity and household income were associated with less desirable eating behaviours. Social disparities in eating behaviours exist among Canadian youth, emphasizing the need for targeted health promotion interventions to address the structural factors contributing to dietary inequities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".