Socioeconomic indicators and frequency of traditional food, junk food, and fruit and vegetable consumption amongst Inuit adults in the Canadian Arctic
Bibliographic record
Abstract
BACKGROUND: Increasing consumption of non-nutrient-dense foods (NNDF), decreasing consumption of traditional foods (TF) and low consumption of fruit and vegetables (FV) may contribute to increasing chronic disease rates amongst Inuit. The present study aimed to assess the daily frequency and socioeconomic and demographic factors influencing consumption of TF, FV and NNDF amongst Inuit adults in Nunavut, Canada. METHODS: Using a cross-sectional study design and random household sampling in three communities in Nunavut, a food frequency questionnaire developed for the population was used to assess frequency of NNDF, TF and FV consumption amongst Inuit adults. Socioeconomic status (SES) was assessed by education level, ownership of items in working condition, and whether or not people in the household were employed or on income support. Mean frequencies of daily consumption were compared across gender and age groups, and associations with socioeconomic indicators were analysed using logistic regression. RESULTS: Two hundred and eleven participants (36 men, 175 women; mean (standard deviation) ages 42.1 (15.0) and 42.2 (13.2) years, respectively; response rate 69-93%) completed the study. Mean frequencies of consumption for NNDF, TF and FV were 6.3, 1.9 and 1.6 times per day, respectively. On average, participants ≤50 years consumed NNDF (P=0.003) and FV (P=0.01) more frequently and TF (P=0.01) less frequently than participants >50 years. Education was positively associated with FV consumption and negatively associated with TF consumption. Households on income support were more likely to consume TF and NNDF. CONCLUSIONS: These results support the hypothesis that the nutrition transition taking place amongst Inuit in Nunavut results in elevated consumption of NNDF compared with TF and FV.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".