Comparison of a semi-quantitative food frequency questionnaire with 24-hour dietary recalls to assess dietary intake of adult Kuwaitis
Bibliographic record
Abstract
The lifestyle of the residents of Arab and Persian Gulf countries is changing due to globalization. They consume more fat, meat, fast foods, and sugar than before. 1 To assess an individuals’ dietary intake, a valid dietary tool is needed. We developed such an instrument for the population of the United Arab Emirates (UAE). The foods consumed in UAE, however, are influenced by its large multicultural immigrant population, and are not typical of those eaten in other countries in the region. Thus, it was necessary to develop such an instrument. It is necessary to validate any food frequency questionnaires (FFQ) that are developed for specific populations, as incorrect information may lead to false associations between dietary factors and diseases or disease-related markers. 2 We developed a semiquantitative food frequency questionnaire (SFFQ) and accompanying food composition database for Kuwait. The SFFQ effectively captures the type and quantity of food the population in Kuwait usually eat, and their frequency. When these data are combined with the food composition table for Kuwait, it is possible to determine long-term nutrient intake for this population. The developed SFFQ listed standard portions of food items traditionally consumed in Kuwait, and intake frequencies for the food items consisted of 9 categories ranging from “never/once a month” to “more than 6
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".