Validity and reliability of the Block98 food-frequency questionnaire in a sample of Canadian women
Bibliographic record
Abstract
OBJECTIVE: To assess the validity and reliability of the most recent adaptation of Block's full-diet food-frequency questionnaire (FFQ) among a sample of Canadian women. DESIGN: Participants completed a self-administered FFQ (FFQ1), two unannounced 24-hour recalls (weekday and weekend) and a second FFQ (FFQ2) between October 2003 and February 2004. FFQs and recalls were analysed for 32 nutrients using Block Dietary Data Systems and the University of Minnesota's Nutrient Data System. Mean and median intakes were computed, along with crude and deattenuated Pearson correlation coefficients between FFQ1 and the average of two recalls (validity) and between FFQ1 and FFQ2 (reliability). SETTING: Ontario, Canada. SUBJECTS: A random population-based sample (n = 166) of women aged 25 to 74 years. RESULTS: One hundred and fifteen (69%) women completed FFQ1, 96 completed FFQ1 and both recalls, and 93 completed both FFQs, about 56 days apart. Mean intakes were similar for most nutrients. FFQ reliability was high, with Pearson correlation coefficients having a median of 0.75, ranging from 0.57 to 0.90 (macronutrients) and from 0.65 to 0.88 (micronutrients from supplements and food). FFQ validity was moderate to high, with deattenuated Pearson correlation coefficients having a median of 0.59, ranging from 0.11 to 0.73 (macronutrients) and from 0.50 to 0.76 (micronutrients from supplements and food). Our micronutrient correlations were similar to or higher than those of other studies that included supplements. Two correlations <0.40 were associated with fats. CONCLUSIONS: The validity and reliability of this full-diet version of the Block FFQ were moderate to high, supporting its use in future studies among Canadian women.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".