Food frequency questionnaire assessing traditional food consumption in Dene/Métis communities, Northwest Territories, Canada
Bibliographic record
Abstract
Food Frequency Questionnaires (FFQ) can be used to document food consumption and to estimate the intake of contaminants for Indigenous populations. The objective of this project was to refine and implement an FFQ to estimate the consumption of traditional locally harvested foods for Dene/Métis in the Northwest Territories, Canada. The strategy consisted of: 1) refining the FFQ through three focus groups and, 2) implementing the FFQ in Indigenous communities. Participants were asked to complete the FFQ using an iPad to document the types of traditional foods consumed over the past 12 months, as well as the consumption frequency, the portion size, and the preparation methods. Focus groups supported the refinement of the FFQ on the format, the list of foods, and the preparation methods listed in the questionnaire. The refined FFQ was then implemented with participants (n = 237). Findings indicated that the traditional foods most frequently consumed were moose, whitefish and lake trout. Participants who consumed fish and land animals reported, on average, a portion size for one serving of between 126 and 143 g, depending on age and sex. These findings increase knowledge of the current traditional food consumption of Dene/Métis communities and will support the assessment of contaminant exposure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".