Intakes of nutrients and food categories in Canadian children and adolescents across levels of sugars intake: cross-sectional analyses of the Canadian Community Health Survey 2015 Public Use Microdata File
Bibliographic record
Abstract
Dietary recommendations to reduce sugars consumption may influence choices of sugars-containing foods and affect the intake of key micronutrients. We compared intakes of nutrients and food sources stratified by quintiles of total sugars in Canadian children (2–8 y) and adolescents (9–13 y, 14–18 y) using 24-hour dietary recalls from the 2015 Canadian Community Health Survey-Nutrition. Energy intakes did not differ across quintiles of sugars intake. Those with lower sugars intakes (Q1/Q3) generally had higher protein, fat, sodium, niacin, folate, and zinc and lower vitamin C compared with those with the highest sugars intakes (Q5). Q1 also had lower potassium but higher saturated fat compared with Q5. Further, Q1 generally had higher protein, fats, and niacin compared with Q3, while children in Q3 had higher potassium and riboflavin and older adolescents had higher calcium and fibre. Q5 had highest intakes of multiple sugar-containing food categories (e.g., fruit, confectionary, milks, cakes/pies/pastries), with higher sugars-sweetened beverages in adolescents. Q3 had higher fruit, milks, and fruit juice compared with Q1 and lower sugars/syrups/preserves, confectionary, and fruit juices compared with Q5. Certain nutrient-dense food sources of sugars (fruit, milks) may help increase key nutrients (potassium, calcium, fibre) in older adolescents with low sugars intakes. However, in those with the highest sugars intakes, nutrient-poor foods may displace nutrient-dense foods. Novelty: Canadian children and adolescents with lower sugars intake have better intakes of some nutrients. Energy intakes did not differ across sugars intake. Older adolescents with mean intakes of total sugars had better intakes of some key nutrients (potassium, calcium, fibre).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".