Validity and Reproducibility of a Semi-Quantitative Food-Frequency Questionnaire Designed to Measure the Nutrient Intakes of Canadian South Asian Infants at 12 Months of Age
Bibliographic record
Abstract
Purpose: Validated methods to assess diet of non-European infants are sparse. We assessed the validity and reliability of a semi-quantitative food-frequency questionnaire (FFQ) for South Asian infants in Canada. Methods: We developed an 80-item FFQ to assess infant nutrient intake in the South Asian Birth Cohort study (START). Caregivers completed the FFQ twice along with two 24-hour diet recalls. We measured infant plasma ferritin to cross-validate reported iron intake. We evaluated validity using Spearman’s rho (ρ), and reliability using the intraclass correlation coefficient. Results: Seventy-six caregivers provided 2 FFQs and 2 24-hour diet recalls. Energy-adjusted, de-attenuated correlations between the FFQs and 24-hour diet recalls ranged from −0.29 (monounsaturated fat) through 1.00 (cholesterol). The FFQ overestimated energy intake by 128%. Iron intake by 24-hour diet recalls correlated with plasma ferritin (r = 0.41; P = 0.01; n = 37), but iron intake by FFQ did not. The average reproducibility coefficient of the FFQ ranged from 0.24 (macronutrients) to 0.65 (minerals). Conclusions: Among South Asian infants living in Canada, at least 2 days of diet recall completed with the primary caregiver yields more valid and reproducible estimates of nutrient intakes than a semi-quantitative FFQ, and it highlights that careful selection of FFQ portion sizes is important for assessing dietary intake with an FFQ.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".