Nutrient intakes and weight status of adult Canadian breakfast consumers versus non‐consumers, and of those consuming breakfasts with versus without ready‐to‐eat cereal
Bibliographic record
Abstract
Higher nutrient intakes are reported in breakfast consumers vs. non‐consumers, and in those who include ready‐to‐eat cereal (RTEC) at breakfast vs. those who do not. We examined these differences using Day 1 data from adult participants (age ≥19; n = 20,157) in the Canadian Community Health Survey Cycle 2.2 (2004), a nationally‐representative sample. Overall, 90% of Canadian adults reported eating breakfast, increasing from 82% (age 19–30) to 97% (age 71+). Breakfast consumers had higher ( P ≤ 0.01) energy intakes than non‐consumers (mean ± SE: 2112 ± 15 vs. 1933 ± 34 kcal/d), but body mass index (BMI; from measured height and weight) and weight status (odds ratio for overweight/obesity) did not differ. They also had higher energy‐adjusted intakes of protein, carbohydrate (CHO), fibre, cholesterol, calcium (Ca), phosphorus (P), magnesium (Mg), iron (Fe), potassium (K), and vitamins A, B 1 , B 2 , B 3 , B 6 , folate, C and D. Among breakfast consumers, 23% included RTEC. Energy intakes and weight status of RTEC consumers and non‐consumers did not differ, but RTEC consumers had higher intakes of CHO, fibre, Ca, P, Mg, Fe, K, zinc, vitamins B 1 , B 2 , B 6 , folic acid and D, and lower cholesterol and fat intakes. Breakfast made a positive contribution to adult Canadians’ nutrient intakes, and those who ate RTEC breakfasts had a more favourable nutrient profile than those who ate other breakfasts. (Supported by Kellogg Canada Inc.)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".