Traditional Food, Health, and Diet Quality in Syilx Okanagan Adults in British Columbia, Canada
Bibliographic record
Abstract
In Canada, store-bought food constitutes the majority of First Nations (FN) people's diets; however, their traditional foods (TF; wild fish, game, fowl, and plants) remains vital for their health. This study compares health indicators and diet quality among 265 Syilx Okanagan adults according to whether or not they reported eating TF during a 24-h dietary recall. Three methods assessed diet quality: nutrient intakes and adequacy, Healthy Eating Index (HEI-C), and contributions of ultra-processed products (UPP) to %energy using the NOVA classification. Fifty-nine participants (22%) reported eating TF during the dietary recall; TF contributed to 13% of their energy intake. There were no significant differences in weight status or prevalence of chronic disease between TF eaters and non-eaters. TF eaters had significantly higher intakes of protein; omega-3 fatty acids; dietary fibre; copper; magnesium; manganese; phosphorus; potassium; zinc; niacin; riboflavin; and vitamins B6, B12, D, and E than non-eaters. TF eaters also had significantly better diet quality based on the HEI-C and the %energy from UPP. Findings support that TF are critical contributors to the diet quality of FN individuals. Strength-based FN-led interventions, such as Indigenous food sovereignty initiatives, should be promoted to improve access to TF and to foster TF consumption.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".