Dietary patterns, approaches, and multicultural perspectiveThis is one of a selection of papers published in the CSCN–CSNS 2009 Conference, entitled Can we identify culture-specific healthful dietary patterns among diverse populations undergoing nutrition transition?
Bibliographic record
Abstract
Nutrition research has traditionally focused on single nutrients in relation to health. However, recent appreciation of the complex synergistic interactions among nutrients and other food constituents has led to a growing interest in total dietary patterns. Methods of measurement include summation of food or nutrient recommendations met, such as the United States Department of Agriculture Healthy Eating Index; data-driven approaches--principal components (PCA) and cluster analyses--which describe actual intake patterns in the population; and, most recently, reduced rank regression, which defines linear combinations of food intakes that maximally explain intermediate markers of disease. PCA, a form of factor analysis, derives linear combinations of foods based on their intercorrelations. Cluster analysis groups individuals into maximally differing eating patterns. These approaches have now been used in diverse populations with good reproducibility. In contrast, because it is based on associations with outcomes rather than on coherent behavioral patterns, reduced rank regression may be less reproducible, but more research is needed. However, it is likely to yield useful information for hypothesis generation. Together, the focus on dietary patterns has been fruitful in demonstrating the powerful protective associations of healthy or prudent dietary patterns, and the higher risk associations of Western or meat and refined grains patterns. The field, however, has not fully addressed the effects of diet in subpopulations, including ethnic minorities. Depending on food group coding, subdietary patterns may be obscured or artificially separated, leading to potentially misleading results. Further attention to the definition of the dietary patterns of different populations is critical to providing meaningful results. Still, dietary pattern research has great potential for use in nutrition policy, particularly as it demonstrates the importance of total diet in health promotion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".