Development of a semi-quantitative food frequency questionnaire for use in United Arab Emirates and Kuwait based on local foods
Bibliographic record
Abstract
BACKGROUND: The Food Frequency Questionnaire (FFQ) is one of the most commonly used tools in epidemiologic studies to assess long-term nutritional exposure. The purpose of this study is to describe the development of a culture specific FFQ for Arab populations in the United Arab Emirates (UAE) and Kuwait. METHODS: We interviewed samples of Arab populations over 18 years old in UAE and Kuwait assessing their dietary intakes using 24-hour dietary recall. Based on the most commonly reported foods and portion sizes, we constructed a food list with the units of measurement. The food list was converted to a Semi-Quantitative Food Frequency Questionnaire (SFFQ) format following the basic pattern of SFFQ using usual reported portions. The long SFFQ was field-tested, shortened and developed into the final SFFQ. To estimate nutrients from mixed dishes we collected recipes of those mixed dishes that were commonly eaten, and estimated their nutritional content by using nutrient values of the ingredients that took into account method of preparation from the US Department of Agriculture's Food Composition Database. RESULTS: The SFFQs consist of 153 and 152 items for UAE and Kuwait, respectively. The participants reported average intakes over the past year. On average the participants reported eating 3.4 servings/d of fruits and 3.1 servings/d of vegetables in UAE versus 2.8 servings/d of fruits and 3.2 servings/d of vegetables in Kuwait. Participants reported eating cereals 4.8 times/d in UAE and 5.3 times/d in Kuwait. The mean intake of dairy products was 2.2/d in UAE and 3.4 among Kuwaiti. CONCLUSION: We have developed SFFQs to measure diet in UAE and Kuwait that will serve the needs of public health researchers and clinicians and are currently validating those instruments.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".