Breakfast Quality of Preschool-aged Canadian Children
Bibliographic record
Abstract
Purpose: To investigate the breakfast quality of preschool-aged children through a comparison of their energy and nutrient intakes at breakfast to published benchmarks for a balanced breakfast. Methods: Dietary data were collected for 163 children aged 3–5 years enrolled in the Guelph Family Health Study using one parent-reported online 24-hour recall and analyzed for energy and nutrient intakes. Breakfast quality was assessed by tallying the frequency of participants whose nutrient and energy intakes at their breakfast meal met the recommendations for a balanced breakfast established by the International Breakfast Research Initiative (IRBI). Results: Almost all participants (98%) consumed breakfast, and most participants (82.5%) met the energy IRBI recommendation. However, the majority of participants did not meet the IRBI recommendations for breakfast intakes of most macronutrients and micronutrients. In particular, fewer than 25% of participants met the IRBI recommendations for breakfast intakes of dietary fibre, niacin, folate, vitamin C, calcium, potassium and zinc. Conclusions: Almost all preschool-aged children in this study consumed breakfast, but the nutritional quality of their breakfast did not meet recommendations for most nutrients. These results can inform nutrition education and intervention programs for children that aim to improve the nutritional quality of breakfast.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".