Changing Dietary Patterns in the Canadian Arctic: Frequency of Consumption of Foods and Beverages by Inuit in Three Nunavut Communities
Bibliographic record
Abstract
BACKGROUND: Inuit in Arctic regions are experiencing a rapid diet and lifestyle transition. There are limited data on food consumption patterns among this unique population, raising concerns about assessing the risk for the development of diet-related chronic diseases. OBJECTIVE: To assess the current frequency of consumption of foods and beverages among Inuit in Nunavut, Arctic Canada. METHODS: A cross-sectional dietary study was conducted among randomly selected Inuit adults from three communities in Nunavut using a validated quantitative food frequency questionnaire. The participants were 175 women and 36 men with median (IQR) ages of 41.0 (32.5-48.5) and 40.1 (30.0-50.0) years, respectively. The mean and median frequencies of consumption over a 30-day period were computed for 147 individual food items and grouped as foods or beverages. RESULTS: The 30 most frequently consumed foods were identified. Non-nutrient-dense foods (i.e., high-fat and high-sugar foods) were the most frequently consumed food group (median intake, 3.4 times/day), followed by grains (2.0 times/day) and traditional meats (1.7 times/day). The frequency of consumption of fruits (0.7 times/day) and vegetables (0.4 times/day) was low. The median values for the three most frequently consumed food items were sugar or honey (once/day), butter (0.71 times/day), and Coffee-mate (0.71 times/day). Apart from water, coffee, and tea, the most frequently consumed beverages were sweetened juices (0.71 times/day) and regular pop (soft drinks) (0.36 times/day). This study showed that non-nutrient-dense foods are consumed most frequently in these Inuit communities. CONCLUSIONS: The results have implications for dietary quality and provide useful information on current dietary practices to guide nutritional intervention programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 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) | 0.001 | 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".