Ethnic disparities among food sources of energy and nutrients of public health concern and nutrients to limit in adults in the United States: NHANES 2003–2006
Bibliographic record
Abstract
BACKGROUND: Identification of current food sources of energy and nutrients among US non-Hispanic whites (NHW), non-Hispanic blacks (NHB), and Mexican American (MA) adults is needed to help with public health efforts in implementing culturally sensitive and feasible dietary recommendations. OBJECTIVE: The objective of this study was to determine the food sources of energy and nutrients to limit [saturated fatty acids (SFA), added sugars, and sodium] and nutrients of public health concern (dietary fiber, vitamin D, calcium, and potassium) by NHW, NHB, and MA adults. DESIGN: This was a cross-sectional analysis of a nationally representative sample of NWH (n=4,811), NHB (2,062), and MA (n=1,950) adults 19+ years. The 2003-2006 NHANES 24-h recall (Day 1) dietary intake data were analyzed. An updated USDA Dietary Source Nutrient Database was developed using current food composition databases. Food grouping included ingredients from disaggregated mixtures. Mean energy and nutrient intakes from food sources were sample-weighted. Percentages of total dietary intake contributed from food sources were ranked. RESULTS: Multiple differences in intake among ethnic groups were seen for energy and all nutrients examined. For example, energy intake was higher in MA as compared to NHB; SFA, added sugars, and sodium intakes were higher in NHW than NHB; dietary fiber was highest in MA and lowest in NHB; vitamin D was highest in NHW; calcium was lowest in NHB; and potassium was higher in NHW as compared to NHB. Food sources of these nutrients also varied. CONCLUSION: Identification of intake of nutrients to limit and of public health concern can help health professionals implement appropriate dietary recommendations and plan interventions that are ethnically appropriate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".