Food expenditure patterns in the Canadian Arctic show cause for concern for obesity and chronic disease
Bibliographic record
Abstract
BACKGROUND: Little is understood about the economic factors that have influenced the nutrition transition from traditional to store-bought foods that are typically high in fat and sugar amongst people living in the Canadian Arctic. This study aims to determine the pattern of household food expenditure in the Canadian Arctic. METHOD: Local food prices were collected over 12 months in six communities in Nunavut and the Northwest Territories. Dietary intake data were collected from 441 adults using a validated quantitative food frequency questionnaire. Money spent on six food groups was calculated along with the cost of energy and selected nutrients per person. RESULTS: Participants spent approximately 10% of total food expenditure on each of the food groups of fruit/vegetables, grains and potatoes, and dairy, 17% on traditional meats (e.g. caribou, goose, char, and seal liver), and 20% on non-traditional meats (e.g. beef, pork, chicken, fish, and processed meats). Non-nutrient-dense foods (NNDF) accounted for 34% of food expenditure. Younger participants (<30 years) spent more on NNDF and less on traditional meats compared with the older age groups. Participants with higher levels of formal education spent more on fruit and vegetables and less on traditional meats, when compared with participants with lower levels of formal education. CONCLUSIONS: Participants spent most household income on NNDF, a possible consequence of generation discrepancy between younger and older participants. The tendency toward NNDF, particularly among youth, should be addressed with an assessment of predictive factors and the development of targeted approaches to population-based interventions.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.003 | 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".