Innu Food Consumption Patterns: Traditional Food and Body Mass Index
Bibliographic record
Abstract
PURPOSE: Food consumption patterns of an Innu community were described and the benefits of traditional food (TF) were investigated in relation to body mass index (BMI). METHODS: A cross-sectional study was conducted using food frequency and 24-hour recall questionnaires to evaluate consumption patterns (n=118) and to assess energy and nutrient intakes from TF and store-bought food (SBF) (n=161). Body mass index was calculated with a sub-sample of 45 participants. RESULTS: Mean yearly TF meal consumption was significantly related to age (p=0.05). Participants reporting high TF and low SBF consumption presented with a normal body weight (BMI=24.1) at the lower quartile and a slightly overweight status (BMI=25.8) at the median. Mean values for protein and carbohydrate intake were higher than the Dietary Reference Intakes, whereas dietary fibre intake was below these guidelines for both genders. Store-bought food provided higher levels of energy and nutrients, except for protein. CONCLUSIONS: Although Innu consume high amounts of TF and SBF, a lack of some essential nutrients was observed. Because TF intake was related to a tendency toward a lower BMI, a combined, targeted diet could be proposed. Health services could reinforce the importance of TF consumption and promote traditional dietary practices that offer advantages at many levels.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".