Improving vitamin A and D intake among Inuit and Inuvialuit in Arctic Canada: evidence from the Healthy Foods North study
Bibliographic record
Abstract
BACKGROUND: People in Arctic Canada are undergoing a nutritional transition and increased prevalence of chronic disease. The Healthy Foods North diet and physical activity intervention was developed in 2007-2008 while working with populations in six communities in Nunavut and the Northwest Territories, Canada. METHODS: Four communities received the 1-year intervention (eg, conducting workshops, cooking classes and walking clubs) and two communities served as controls. Among the 263 adult evaluation participants, food frequency questionnaires were used to assess dietary intake at baseline and postintervention. Changes in mean nutrient intakes, nutrient density and dietary adequacy from baseline to postintervention were determined. The intervention impact on nutrient intakes was assessed through multivariate linear regression analysis. RESULTS: Post-intervention assessment showed a reductions in total fat, saturated, monounsaturated and polyunsaturated fatty acids, and increases in iron intake, only in the intervention group. More than a 4%-increase in the percentage of adherence to vitamins A and D recommendations was observed in the intervention group. After adjusting the regression models, respondents in the intervention communities significantly reduced their energy intake and increased their vitamins A and D intake. CONCLUSIONS: The Healthy Foods North is an effective programme to improve dietary quality among populations of the Canadian Arctic. Long-term interventions are expected to be important factors in the prevention of diet-related chronic diseases in these communities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".