Validation of a quantitative food frequency questionnaire for Inuit population in Nunavut, Canada
Bibliographic record
Abstract
BACKGROUND: Validation of a quantitative food frequency questionnaire (QFFQ) developed specifically for Inuit is necessary to determine its usefulness in assessing dietary intake and adequacy and in identifying dietary risk factors for chronic disease in this population. METHODS: Seventy-five randomly selected Inuit adults in Nunavut, Canada, were recruited. Mean daily intake of nutrients from one to three 24-h recalls was used as the reference to measure QFFQ validity. Crude and energy-adjusted Spearman rank correlations (ρ), cross classification and weighted kappa were computed as measures of concordance. Bland-Altman plotting was used for additional assessment. RESULTS: Excluding four participants with daily energy intake of >25.1 MJ, 71 participants were included in the analysis. For all nutrients, mean daily intake from the QFFQ was higher than the recall. ρ's for macronutrients were in the range 0.71 for carbohydrate to 0.25 for protein. The best ρ amongst micronutrients was observed for vitamin C (0.66). Overall correlation between the two dietary tools improved after correction for within-person variance (from 0.46 to 0.49), although adjusting for energy did not improve the overall coefficient. When nutrient intakes were categorised into quartiles, the QFFQ and 24-h recalls indicated relative agreement proportion (same or adjacent quartiles) of 83% for energy, 94% for total sugar, 83% for macronutrients and 77% for micronutrients. Bland-Altman plots showed a tendency for increased scatter of the differences in nutrients at higher intakes. CONCLUSIONS: The QFFQ developed is valid and can be used to assess usual dietary intake and dietary adequacy, determine the contribution of foods to specific nutrient intakes, and identify dietary risk factors for chronic disease amongst this population.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| 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".