Assessing diet and lifestyle in the Canadian Arctic Inuit and Inuvialuit to inform a nutrition and physical activity intervention programme
Bibliographic record
Abstract
Inuit in Nunavut (NU) and Inuvialuit in the Northwest Territories (NWT), Canada, were traditionally nomadic peoples whose culture and lifestyle were founded on hunting and gathering foods from the local environment, primarily land and marine mammals. Lifestyle changes within the last century have brought about a rapid nutrition transition, characterised by decreasing consumption of traditional foods and an associated increase in the consumption of processed, shop-bought foods. This transition may be attributed to a multitude of factors, such as acculturation, overall food access and availability, food insecurity and climate change. Obesity and risk for chronic disease are higher in the Canadian Arctic population compared with the Canadian national average. This present review describes the study population and methodologies used to collect data in order to study the nutrition transition amongst Aboriginal Arctic populations and develop Healthy Foods North (HFN), a novel, multi-institutional and culturally appropriate programme that aims to improve dietary adequacy and reduce risk of chronic disease. Included in this special issue of the Journal of Human Nutrition and Dietetics are papers describing dietary intake patterns, physical activity levels, dietary behaviours, chronic disease prevalence and psychosocial factors that potentially mediate behaviour. A further paper describes how these data were utilised to inform and develop Healthy Foods North.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".