<i>Development and Validation</i>of a Food Frequency Questionnaire
Bibliographic record
Abstract
Regular diet monitoring requires a tool validated in the target population. A 73-item, semiquantitative, self-administered food frequency questionnaire (FFQ), was adapted in French and English from the Block National Cancer Institute Health Habits and History Questionnaire. The FFQ was used to capture usual long-term food consumption among adults living in Quebec. A representative sample of adults aged 18 to 82 (57% female) was recruited by random digit dialling in the Montreal region. Approximately 64% of recruits completed and returned the instrument by mail (n=248). The FFQ was validated in a subsample (n=94, 61% female) using four nonconsecutive food records (FRs). Median energy intakes (in kcal) for men and women, respectively, were FFQ (total sample) 2,112 and 1,823, FFQ (subsample) 2,137 and 1,752, and FR (subsample) 2,510 and 1,830. Spearman correlation analyses between FFQ and FR nutrients were positive (with r ranging from 0.32 for folate to 0.58 for saturated fatty acids) and statistically significant (p<0.001), with better results among women. On average, cross-classification of energy and 24 nutrients from the FFQ and means of four FRs placed 39% into identical quartiles and 78% into identical and contiguous quartiles, with only 4% frankly misclassified. These results suggest that the FFQ is a relatively valid instrument for determining usual diet in Quebec adults.
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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 | 0.008 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".