Development and Validation of Vitamin D- Food Frequency Questionnaire for Moroccan Women of Reproductive Age: Use of the Sun Exposure Score and the Method of Triad’s Model
Bibliographic record
Abstract
This cross-sectional study aimed to develop and validate a vitamin D food frequency questionnaire (VitD-FFQ) to assess vitamin D intake in Moroccan women of reproductive age. Using the method of triads, the VitD-FFQ was validated against seven-day dietary records (7d-FR) and 25-hydroxyvitamin D (25(OH)D) as a biomarker of vitamin D status in 152 women (aged 18–45 years). Participants’ sun exposure scores (SES) were assessed using a specific questionnaire (SEQ). Predictors of vitamin D status were identified via linear regression models. Several statistical tests were applied to evaluate the criterion validity of the FFQ against two references methods (7d-FR and the biomarker-serum 25(OH)D). Median (Interquartile range) intakes were 7.10 ± 6.95 µg /day and 6.33 ± 5.02 µg/ day, respectively, for VitD-FFQ and 7d-FR. Vitamin D status was mainly determined by SES (R = 0.47) and vitamin D absolute food intakes derived by the VitD-FFQ (R = 0.56), which demonstrated a more significant prediction ability compared to 7d-FR (R = 0.36). An agreement was observed between the VitD-FFQ and 7d-FR (BA index of 3.29%) with no proportional bias (R2 = 0.002, p = 0.54). <10% of participants were incorrectly classified, and weighted kappa statistics showed that VitD-FFQ had an acceptable ranking ability compared to the 7d-FR and the biomarker. The validity coefficient for the VitD-FFQ was high: ρQR = 0.90 (95%CI: 0.89–0.92), and a range from 0.46 to 0.90. Adjustment for the participants’ SES and BMI (body mass index) improved the biomarker’s validity coefficient (ρRB 0.63 (95% CI 0.39–0.82). Our results indicate that the VitD-FFQ is valid for estimating absolute vitamin D intake in Moroccan women of reproductive age.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".