Effect of Age and Ethnic Groups on Diet Quality
Bibliographic record
Abstract
The role of age and ethnic groups (Mexican‐American MA versus European‐American EA) on diet quality was examined. Stratified sampling (ethnicity, sex, age, neighborhood, and disease status) was used to collect data from 1,319 subjects living in central Texas using 2 24‐hour recalls (58.8% MAs, 42.7% males). MANOVA was performed to determine if panel of nutrients were affected by age and ethnicity, controlling for sex, education level, BMI, and number of exercise days. Age and ethnicity had appreciable and significant effects on the panel of nutrients. Age explained 9.3% and ethnicity explained 3.9% of the total variance across nutrients. Regardless of ethnicity, % vitamin D intake (p<.0001) and % calcium intake (p<.0001) decreased as age increased. Older subjects (51 and older) were only meeting 24% or less of RDA for vitamin D and only 50% of RDA for calcium. MAs consumed more proteins (p<.0001) and less carbohydrates (p<.0001) than EAs. Our study showed that our older subjects were significantly deficient in vitamin D and calcium. Although ethnicity did explain some differences in nutrient intakes in our subjects, our study underscores the importance of nutrition education for community dwelling elders to eat healthier and improve the quality of their diets.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".