Dietary Sources of Calcium, Vitamin D, and the Pattern of Dairy Products Consumption in Five Ethnic Groups in the United States
Bibliographic record
Abstract
The objective of this study was to describe dietary sources of calcium and vitamin D among five ethnic groups in the United States. Cross-sectional dietary data were collected using a quantitative food frequency questionnaire from 186,916 participants in the Multiethnic cohort representing five ethnic groups (African American, Latino, Japanese American, Native Hawaiian, and Caucasian), aged 45-75 years living in Los Angeles County and the state of Hawaii between 1993 and 1996. Nutrient intakes for calcium and vitamin D were analyzed based on a unique food composition table which was extended and adapted from the USDA food composition database. Dairy products were the greatest contributor to calcium intake in all groups, but the percent contribution varied considerably between ethnic sex groups from 18.6% (Japanese American men) to 37.8% (Caucasian women). Dairy products were also the greatest contributors to vitamin D intake among all ethnic-sex groups except Native Hawaiian and Japanese American men, for whom fish was the top contributor (40.7 and 42.5% respectively). Low-fat milk was the top source (16.0-21.9%) of dairy products in all ethnic-sex groups except Japanese American men and women and Caucasian women. The data identified dietary sources that can be targeted by nutrition intervention programs and dietitians working with ethnic/racial populations at high risk of inadequate intake of calcium and vitamin D.
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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 | 0.000 |
| 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) | 0.000 | 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".