How Do Meals and Snacks Consumed in Childcare Contribute to Children’s Food and Nutrient Intakes?
Bibliographic record
Abstract
Purpose: To assess the dietary contributions of foods consumed in childcare (childcare centre, preschool, or out-of-home childcare) and compare nutrient and food intakes between locations (in childcare vs away from childcare). Method: Dietary intakes of 46 children (1.5–4 years) were assessed with parent-reported 24-hour recalls occurring on a childcare day. The contribution of each nutrient and food group consumed in childcare to its total daily intake was calculated as a proportion and presented as a percentage. Mean nutrient and food group intake densities (per 1000 kcal) were compared between childcare and away-from-childcare locations using linear regression. Results: Foods consumed at childcare contributed to 46% of children’s daily energy intake. Relative to energy, foods consumed in childcare provided proportionally high intakes of vitamin A (56%), fruit (56%), vegetables (54%), whole grains (53%), and legumes (71%) but proportionally low intakes of added sugars (30%), thiamin (38%), vitamin D (40%), iron (40%), and meat and alternatives (38%). Significant differences in nutrient densities were found for vitamin A (higher in childcare) as well as added sugars, thiamin, vitamin B6, and iron (all lower in childcare). Conclusions: These data can provide evidence to inform the development of tailored interventions to improve young children’s diets across multiple settings.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".