Dietary intake and development of a quantitative food-frequency questionnaire for a lifestyle intervention to reduce the risk of chronic diseases in Canadian First Nations in north-western Ontario
Bibliographic record
Abstract
OBJECTIVES: To characterise the diet of First Nations in north-western Ontario, highlight foods for a lifestyle intervention and develop a quantitative food-frequency questionnaire (QFFQ). DESIGN: Cross-sectional survey using single 24 h dietary recalls. SETTING: Eight remote and semi-remote First Nations reserves in north-western Ontario. SUBJECTS: 129 First Nations (Oji-Cree and Ojibway) men and women aged between 18 and 80 years. RESULTS: The greatest contributors to energy were breads, pasta dishes and chips (contributing over 20 % to total energy intake). 'Added fats' such as butter and margarine added to breads and vegetables made up the single largest source of total fat intake (8.4 %). The largest contributors to sugar were sugar itself, soda and other sweetened beverages (contributing over 45 % combined). The mean number of servings consumed of fruits, vegetables and dairy products were much lower than recommended. The mean daily meat intake was more than twice that recommended. A 119-item QFFQ was developed including seven bread items, five soups or stews, 24 meat- or fish-based dishes, eight rice or pasta dishes, nine fruits and 14 vegetables. Frequency of consumption was assessed by eight categories ranging from 'Never or less than one time in one month' to 'two or more times a day'. CONCLUSION: We were able to highlight foods for intervention to improve dietary intake based on the major sources of energy, fat and sugar and the low consumption of fruit and vegetable items. The QFFQ is being used to evaluate a diet and lifestyle intervention in First Nations in north-western Ontario.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".