Findings on dietary patterns in different groups of African origin undergoing nutrition transitionThis 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
In population groups undergoing nutrition transition, it is important to identify healthy and culturally relevant dietary patterns that can be promoted as a means of preventing diet-related chronic diseases. Dietary pattern analyses using data-driven methods are useful for the purpose. The central question addressed in this overview paper is whether there are culture-specific healthy eating patterns, or whether healthy diets may be more universal. Our studies on dietary patterns in population groups of African origin living in Canada (Montreal), Europe (Madrid), and West Africa (urban and rural Benin) inform the discussion. Healthy or prudent, as opposed to Western, eating patterns are identified in several cultures, including groups of African origin. It appears that a limited number of foods predict diet quality and health outcomes in various population groups; in particular, fruit and vegetables, fish, whole-grain cereal, and legumes do so on the protective side, and sweets, processed meats, fried foods, fats and oils, and salty snacks do so on the negative side. Further research on dietary patterns and their healthfulness is required in diverse food cultures. In groups of African origin, traditional diets are healthier than the nontraditional dietary patterns that have evolved with globalization, urbanization, or acculturation, although micronutrient intakes need to improve. Additionally, healthy eating patterns are only feasible if access to food is adequate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".