Ethnic disparities of beverage consumption in infants and children 0–5 years of age; National Health and Nutrition Examination Survey 2011 to 2014
Bibliographic record
Abstract
BACKGROUND: Dietary patterns, including beverage consumption, that are developed during a child's first few years of life have been shown to impact dietary choices made later in life. Authoritative sources provide beverage recommendations for infants and children; however, it is unclear if these guidelines are followed and what, if any, the differences are among races/ethnicities. The objective of this study was to examine beverage consumption to recommendations among children 0-5 months, 6-11 months, 12-23 months, 2-3 years, and 4-5 years. Additionally, examine how these beverage patterns associate with nutrient intake and to determine if differences exist in beverage consumption among race/ethnic groups (Non-Hispanic White, Non-Hispanic Black, Hispanic, and Asian) in children aged 0-23 months, 2-3 years, and 4-5 years. METHODS: Data from the 2011-2014 National Health and Nutrition Examination Survey (NHANES) for children 0-5 years were analyzed (n = 2445). Beverages were classified as follows; milk, 100% juice, diet beverages, sugar sweetened beverages (SSB), and water. RESULTS: Our results show that regardless of race/ethnicity, dietary recommendation were not always followed. Prior to 6 months, 10% of infants consumed any amount of 100% juice; from 6 to 11 months, 17% of young children were drinking any amount of milk. SSB consumption rapidly increased with age, whereas intake of milk and 100% juice declined after 2 to 3 years of age. Non-Hispanic Black young children consumed the most 100% juice from 2 to 3 years and up, exceeding recommended amounts, and throughout all age groups they consumed the least milk and most SSBs. The decreased intake of nutrient-rich beverages with age was associated with lower intakes of under-consumed nutrients of public health concern. By 4-5 years, 32.7% and 93.8% of children were consuming
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".