DEVELOPING A RELATIVELY VALIDATED AND REPRODUCIBLE FOOD FREQUENCY QUESTIONNAIRE IN BAGHDAD, IRAQ
Bibliographic record
Abstract
Nutritional assessment is essential in the evaluation of individuals’ health status. this study seeks to develop and validate a food frequency questionnaire (FFQ) that can be used as a nutritional assessment tool in epidemiological studies. a stratified random sampling enrolled sixty-five participants from Baghdad university college of medicine. participants were asked to fill a four days food upon which the questionnaire was developed. the participants were asked to fill the questionnaire based on their food intake in the last year. the data was entered using a food application, and the data source for the food nutritional values was obtained from different food composition tables. the serving size was assessed based on the Canadian nutritional society guidelines. the validation and reliability of the questionnaire were evaluated by comparing food intake of the records and the questionnaires using paired mean difference and Pearson correlation coefficient. the energy was adjusted using the nutrient density method. the mean difference between the records and questionnaire was 151.3 kcal, 7.61, 10.45, 10.24 gram for energy, fat, carbohydrate, and protein, respectively. the correlation coefficient between the record and the questionnaire was 0.829, 0.583, 0.323, and 0.547 for energy, fat, carbohydrate, and protein, respectively. in conclusion, this is the first valid food frequency questionnaire that was shown to be valid and reliable in providing a nutritional assessment of dietary intake in Iraq. it requires more efforts to be considered as a national tool for dietary assessment.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".