Inequities in dietary intake and eating behaviours among adolescents in Canada
Bibliographic record
Abstract
OBJECTIVE: To provide contemporary evidence of how dietary intake and eating behaviours vary by social positions among adolescents. METHODS: We used survey data collected during the 2020-2021 school year from 52,138 students attending 133 secondary schools in Alberta, British Columbia, Ontario, and Quebec, Canada. Multiple regression models tested whether self-reported indicators of dietary intake and eating behaviours differed by gender, race/ethnicity, and socioeconomic status (SES). RESULTS: Females were more likely than males to skip breakfast, restrict eating, and consume fruit, vegetables, and fast food on more days. Gender-diverse/"prefer not to say" students were more likely to restrict eating than males and the least likely to consume breakfast and drink water daily, and fruits and vegetables regularly. Black and Latin American students were more likely to restrict eating and consume purchased snacks and fast food, and less likely to drink water daily than white and Asian adolescents. Daily breakfast consumption was most likely among Latin American students. Black students were the least likely to report eating breakfast daily and fruits and vegetables regularly. Lower SES was associated with lower odds of eating breakfast and drinking water daily and regular fruit and vegetable consumption, and higher odds of restrictive eating and purchased snack consumption. Fast food consumption had a u-shaped association with SES. CONCLUSION: Results emphasize gender, racial/ethnic, and socioeconomic inequities in the diets and eating behaviours of adolescents. There is a critical need to address the structural factors contributing to inequities and prevent the consequences of dietary disparities.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".