Inadequate diets in an Arctic population undergoing a drastic environmental change
Bibliographic record
Abstract
Acculturation is currently affecting food and nutrient intake of Inuit in Arctic Canada, increasing risk of chronic diseases. There is a lack of current data on dietary adequacy among Inuit in Nunavut. To inform and evaluate a nutritional and lifestyle intervention program, Healthy Foods North, up to three 24‐hour dietary recalls were collected from each participant in one remote community in Nunavut. These data were analyzed to estimate energy and nutrient intake and to determine dietary adequacy, most commonly reported foods, and the top food contributors to energy and selected nutrients. Participants included 76 men and women (mean age 44 years, response rate 68%). Mean daily energy intake was 1,971 kcals for women and 2,215 kcals for men. Intakes of dietary fiber, calcium, and vitamins A, D, and E were below the Dietary Reference Intakes for men and women. The most frequently consumed foods were non‐nutrient dense store‐bought foods, including sugar and sweetened juices/drinks. Traditional foods contributed substantially to protein and iron intake, while store‐bought foods were the primary contributors to total fat, carbohydrate, and sugar. Inadequate intake of many nutrients in this Inuit population indicates an immediate need for a nutritional intervention to improve dietary adequacy and reduce risk factors of chronic disease. Supported by ADA, Government of Nunavut DHSS, and Health Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".