Addressing the public health burden caused by the nutrition transition through the Healthy Foods North nutrition and lifestyle intervention programme
Bibliographic record
Abstract
Dietary inadequacies, low levels of physical activity, excessive energy intake and high obesity prevalence have placed Inuit and Inuvialuit populations of the Canadian Arctic at increased risk of chronic disease. An evidence-based, community participatory process was used to develop Healthy Foods North (HFN), a culturally appropriate nutrition and physical activity intervention programme that aimed to reduce risk of chronic disease and improve dietary adequacy amongst Inuit/Inuvialuit in Nunavut and the Northwest Territories. HFN was implemented over the course of 12 months in a series of seven phases between October 2008 and 2009 (Nunavut) and June 2008 and 2009 (Northwest Territories). Combining behaviour change and environmental strategies to increase both the availability of healthful food choices in local shops and opportunities for increasing physical activity, HFN promoted the consumption of traditional foods and nutrient-dense and/or low energy shop-bought foods, utilisation of preparation methods that do not add fat content, decreased consumption of high-energy shop-bought foods, and increased physical activity. Messages identified in the community workshops, such as the importance of family eating and sharing, were emphasised throughout the intervention. Intervention components were conducted by community staff and included working with shops to increase the stocking of healthy foods, point of purchase signage and promotion in shops and community settings, pedometer challenges in the workplace and use of community media (e.g. radio and cable television advertisements) to reinforce key messages. HFN represents an innovative multilevel approach to the reduction of chronic disease risk factors amongst Inuit and Inuvialuit, based on strong collaboration with local agencies, government and institutions.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".