Consumption of ultra-processed foods and likely impact on human health. Evidence from Canada
Bibliographic record
Abstract
OBJECTIVE: To investigate consumption of ultra-processed products in Canada and to assess their association with dietary quality. DESIGN: Application of a classification of foodstuffs based on the nature, extent and purpose of food processing to data from a national household food budget survey. Foods are classified as unprocessed/minimally processed foods (Group 1), processed culinary ingredients (Group 2) or ultra-processed products (Group 3). SETTING: All provinces and territories of Canada, 2001. SUBJECTS: Households (n 5643). RESULTS: Food purchases provided a mean per capita energy availability of 8908 (se 81) kJ/d (2129 (se 19) kcal/d). Over 61·7 % of dietary energy came from ultra-processed products (Group 3), 25·6 % from Group 1 and 12·7 % from Group 2. The overall diet exceeded WHO upper limits for fat, saturated fat, free sugars and Na density, with less fibre than recommended. It also exceeded the average energy density target of the World Cancer Research Fund/American Institute for Cancer Research. Group 3 products taken together are more fatty, sugary, salty and energy-dense than a combination of Group 1 and Group 2 items. Only the 20 % lowest consumers of ultra-processed products (who consumed 33·2 % of energy from these products) were anywhere near reaching all nutrient goals for the prevention of obesity and chronic non-communicable diseases. CONCLUSIONS: The 2001 Canadian diet was dominated by ultra-processed products. As a group, these products are unhealthy. The present analysis indicates that any substantial improvement of the diet would involve much lower consumption of ultra-processed products and much higher consumption of meals and dishes prepared from minimally processed foods and processed culinary ingredients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".