Healthy Foods North improves diet among Inuit and Inuvialuit women of childbearing age in Arctic Canada
Bibliographic record
Abstract
BACKGROUND: Healthy Foods North (HFN) is a community-based intervention designed to promote a healthy diet and lifestyle of Inuit and Inuvialuit populations in Arctic Canada. The objective of the present study was to determine the effects of HFN on the nutrient intake of women of childbearing age. METHODS: Six communities in Nunavut (n = 3) and the Northwest Territories (n = 3) were selected for programme implementation; four received a 12-month intervention and two served as controls. Quantitative food frequency questionnaires were used to assess dietary intake at baseline and 1 year post-intervention. Among women participants aged 19-44 years (n = 136), 79 were exposed to the intervention and 57 were not. Mean daily energy and nutrient intake and density were determined. Dietary adequacy was assessed by comparing the women's daily nutrient intakes with dietary reference intakes (DRI). RESULTS: Main outcomes were the pre- to post-intervention changes between intervention and control groups for energy and selected nutrient intakes, nutrient density and dietary adequacy. Among the participants, the intervention had a beneficial effect on vitamin A and D intake. The percentage of individuals with nutrient intakes below the DRI increased from pre- to post-intervention for vitamin A and D in the control group but only for vitamin A in the intervention group. The programme did not have a significant impact on calorie, sugar, or fat consumption. CONCLUSIONS: The HFN programme is effective in mitigating some of the negative impacts of the nutrition transition on dietary adequacy among Inuit and Inuvialuit women of childbearing age.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".