Impact of the Healthy Foods North nutrition intervention program on Inuit and Inuvialuit food consumption and preparation methods in Canadian Arctic communities
Bibliographic record
Abstract
BACKGROUND: The 12-month Healthy Foods North intervention program was developed to improve diet among Inuit and Inuvialuit living in Arctic Canada and assess the impact of the intervention established for the communities. METHODS: A quasi-experimental study randomly selected men and women (≥19 years of age) in six remote communities in Nunavut and the Northwest Territories. Validated quantitative food frequency and adult impact questionnaires were used. Four communities received the intervention and two communities served as delayed intervention controls. Pre- and post-intervention changes in frequency of/total intake of de-promoted food groups and healthiness of cooking methods were determined. The impact of the intervention was assessed using analysis of covariance (ANCOVA). RESULTS: Post-intervention data were analysed in the intervention (n = 221) and control (n = 111) communities, with participant retention rates of 91% for Nunavut and 83% for the Northwest Territories. There was a significant decrease in de-promoted foods, such as high fat meats (-27.9 g) and high fat dairy products (-19.8 g) among intervention communities (all p ≤ 0.05). The use of healthier preparation methods significantly increased (14.7%) in intervention communities relative to control communities. CONCLUSIONS: This study highlights the importance of using a community-based, multi-institutional nutrition intervention program to decrease the consumption of unhealthy foods and the use of unhealthy food preparation methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".