Bibliographic record
Abstract
This is the first study to investigate how food and nutrient intakes vary with the levels of acculturation of Korean Americans using a dietary assessment tool tested for validity and reliability. The respondents were Korean Americans (n 486) resident in the Greater New York metropolitan area, USA. They were divided into two groups according to the total score of acculturation: low- and high-acculturated groups. Using a food-frequency questionnaire and a modified Suinn-Lew Asian self-identity acculturation scale, differences in the frequencies of food items, food groups and nutrient intakes consumed were analysed by level of acculturation. The low-acculturated group tended to consume significantly more rice, mixed rice, soyabean paste chigae (pot stew), saengsun (fish) chigae, kimchi chigae, other fish broiled or baked, eggs, kimchi, spinach, persimmons, and white or brown sugar in coffee or tea. The high-acculturated group demonstrated a statistically greater tendency to consume bread, cereal, spaghetti, ham, green salad, sweetcorn, chocolate, candies and diet soft drinks. The more acculturated an individual, the more significant was the tendency to consume sweets. The more acculturated group had significantly (P<0.05) higher intakes of % energy from total fat, thiamin, vitamin E and folate, while the low-acculturated group consumed greater amounts of Na, niacin and dietary fibre. The information from the present study can be used to describe dietary habits according to various aspects of acculturation, and allows a better understanding of the dynamics of acculturation and dietary habits.
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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 | 0.001 |
| 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".