Child Beverage Consumption in US Early Care and Education Settings, 2008–2020
Bibliographic record
Abstract
OBJECTIVE: Describe young children's beverage intake in early care and education (ECE) settings between 2008 and 2020 across multiple states in the US. METHODS: Multivariable-adjusted, age-stratified estimates of beverage consumption among children aged 12-60 months (n = 4,457) in ECE centers and homes (n = 846). RESULTS: During any given day in ECE, younger children had a 79.7% per-meal probability of consuming milk, 8.9% water, 19.8% 100% juice, and 3.2% sugar-sweetened beverages (SSBs), and a per-meal mean intake of 1.5 oz milk, 1.7 oz water, 2.2 oz 100% juice, and 2.9 oz SSBs. Older children had an 87.2% probability of consuming milk, 0.6% water, 2.9% 100% juice, and 4.2% SSBs, and a mean intake of 4.2 oz milk, 2.3 oz water, 3.6 oz 100% juice, and 5.9 oz SSBs. CONCLUSIONS AND IMPLICATIONS: There is room to improve beverage intake in ECE, with a focus on increasing water and decreasing juice and SSB consumption. These results may justify policies to limit or prohibit juice consumption in ECE.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".