Socio-demographic correlates of ultra-processed food consumption in Canada
Bibliographic record
Abstract
Abstract Objective: To characterise consumption of ultra-processed foods and drinks (UPF) across a range of socio-demographic characteristics of Canadians. Design: Cross-sectional study. The national-level 2015 Canadian Community Health Survey–Nutrition provided data on all foods and drinks consumed on the previous day via a 24-hour dietary recall. All food items were classified according to the type of industrial processing using the NOVA system. Multivariable linear regression models examined associations between a range of socio-demographic characteristics and the mean energy contribution (% of total daily energy intake) from total UPF and UPF subgroups. Setting: The ten Canadian provinces. Participants: Canadians aged 2 or older ( n 20 103). Results: UPF contributed, on average, nearly half (44·9 %) of total daily energy intake of Canadians. Children aged 6–12 and adolescents aged 13–18 consumed over half of total daily energy from UPF (adjusted means of 51·9 % and 50·7 %, respectively). Recent and long-term immigrants consumed a significantly lower share of energy from UPF (adjusted means of 42·2 % and 45·1 %, respectively) compared with non-immigrants (54·4 %), as did the food secure (42·8 %) v . those in moderately (48·1 %) or severely food-insecure households (50·8 %). More modest differences were observed for intake of total UPF and UPF subgroups by sex, education, income adequacy and region of residence. Conclusion: Levels of UPF consumption in 2015 in Canada were pervasive in all socio-demographic groups and highest among children and adolescents, non-immigrants and those living in food-insecure households. These findings can inform public health interventions to reduce UPF consumption and promote healthier diets in various socio-demographic groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".