Patterning of Food Preferences Among Iranian Adults: Results from SEPAHAN Study
Bibliographic record
Abstract
Background: The present study was conducted to evaluate the pattern of food preference among a large sample of Iranian adults. Methods: In a cross-sectional study within the study on the epidemiology of psychological alimentary health and nutrition (SEPAHAN) projects, a total of 6239 of 8694 subjects completed a 106-item food preference questionnaire. Subjects indicated whether they liked, disliked or had gastrointestinal symptoms for each food item separately. They also reported the frequency of consumption for each food item. Results: We observed that presence of some foods such as yogurt, fruits and vegetables in the list of the most preferred food items and presence of kalbas, sausages and chips in the list of the most disliked food items, were representative of healthy dietary pattern in this population. Results also revealed that women liked unhealthy foods more than men ( P value <0.05 for all significant food items). Moreover, in most of the food items, men reported less gastrointestinal symptoms than women ( P value <0.05 for all significant food items). Our findings revealed that smokers disliked most of the healthy food items. We also observed that pregnant women regardless of the trimesters, reported dislike for sweet-tasting food items. Conclusions: More researches are suggested in order to indicate the origins of preferences and recommend some practical alternatives to improve the dietary pattern in society.
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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) | 0.002 | 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".