Current Contribution to Energy and Nutrient Intake from Dairy Foods in Children and Adults Using NHANES, 2015–2018
Bibliographic record
Abstract
BACKGROUND: The Dietary Guidelines for Americans recommends 2-3 servings of dairy a day, but most of the American population consumes less. Multiple factors can influence the intake of dairy including sex, age, ethnicity, and income. OBJECTIVES: This study aims to determine the calorie and nutrient contribution of dairy foods stratified by race/ethnicity in the United States to assess if messages regarding dairy recommendations should be tailored to different populations. METHODS: National Health and Nutrition Examination Survey 2015-2018 (N = 14,851) data were used to calculate the contribution of dairy and dairy products to underconsumed nutrients and nutrients of public health concern stratified by age and race/ethnicity. The population ratio method was used to determine the percentage of contribution of dairy foods to calories and nutrients. RESULTS: Milk was the top source of vitamin D and potassium from the dairy group, whereas cheese was the top source of calcium in children and adults. Both non-Hispanic Black children and adults consumed fewer nutrients from dairy, whereas non-Hispanic Asian children consumed more nutrients from dairy compared with their adult counterparts. CONCLUSIONS: Given the disparity in dairy intake, results suggest that concerted efforts are needed to develop targeted specific messages to different subgroups based on race/ethnicity to promote dairy intake.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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 | 0.001 |
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".