Do knowledge and attitudes affect dietary behaviors in a population undergoing a radical transition in food access, acquisition, and preparation?
Bibliographic record
Abstract
In Arctic Canada, rapid transition in diet and dietary behaviors has contributed to rising chronic disease prevalence among Inuit. Little is known about the patterns of food acquisition and preparation behaviors of Inuit adults and their associations with psychosocial and socioeconomic factors. Surveys were conducted in 3 remote Arctic communities in Nunavut with 266 participants (response rate 70–90%, mean age 41 years). High fat/sugar foods were obtained 2.9 times more frequently on average than healthier foods. Neutral cooking methods (42%) and those adding fat (32%) were more frequently used than healthier methods that reduced fat content (26%). Food intentions were negatively correlated with unhealthy food getting (−0.23, p<0.001), while positively associated with healthy food getting (0.23, p<0.001) and preparation methods (0.16, p=0.01). Higher levels of food knowledge and self‐efficacy were associated with greater intentions and healthier behaviors. Some socioeconomic factors were highly associated with healthier behaviors. This study identified important factors for nutritional and physical activity interventions like Healthy Foods North to consider when targeting this high‐risk population. Supported by ADA, Government of Nunavut DHSS, and Health Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".