Nutrient intakes of Canadian children and adolescents at school by meal occasion and location of food preparation
Bibliographic record
Abstract
Canadian children consume a significant proportion of daily foods at school, do not benefit from any federal school food program, and have historically inadequate diets. Assessment of dietary intakes at school can inform policy discussions for the design, funding, and delivery of school-based nutrition interventions. The objectives were to examine the most recent nationally representative dietary intake data of Canadian children at school by (i) location of food preparation, (ii) meal occasion, and (iii) as a proportion of total daily intakes. Intake data from the first day 24 h dietary recalls of the 2015 Canadian Community Health Survey-Nutrition were examined for children 4–18 years old ( n = 1690). Intakes were reported by location of food preparation and meal occasion and were expressed as means and as a proportion of daily intake. At school, 98.6% of children consumed foods that did not require preparation, while 37.1% consumed foods prepared at home. Lunch and snacks were the meal occasions consumed most often at school, by 85.5% and 66.1% of children. Children consumed 32.6% of their daily energy intake and between 28.4% and 35.6% of daily nutrient intakes at school. School-based nutrition interventions for frequently consumed meal occasions, such as snack or lunch programs, that include foods lower in added sugar and sodium and higher in calcium, fibre, and iron may improve the health of Canadian children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".