Quantifying associations of the dietary share of ultra-processed foods with overall diet quality in First Nations peoples in the Canadian provinces of British Columbia, Alberta, Manitoba and Ontario
Bibliographic record
Abstract
OBJECTIVE: To quantify associations of the dietary share of ultra-processed foods (UPF) with the overall diet quality of First Nations peoples. DESIGN: A cross-sectional analysis of data from the First Nations Food, Nutrition and Environment Study, designed to contribute to knowledge gaps regarding the diet of First Nations peoples living on-reserve, south of the 60th parallel. A multistage sampling of communities was conducted. All foods from 24 h dietary recalls were categorized into NOVA categories and analyses were performed to evaluate the impact of UPF on diet quality. SETTING: Western and Central Canada. SUBJECTS: First Nations participants aged 19 years or older. RESULTS: The sample consisted of 3700 participants. UPF contributed 53·9 % of energy. Compared with the non-UPF fraction of the diet, the UPF fraction had 3·5 times less vitamin A, 2·4 times less K, 2·2 times less protein, 2·3 times more free sugars and 1·8 times more Na. As the contribution of UPF to energy increased so did the overall intakes of energy, carbohydrate, free sugar, saturated fat, Na, Ca and vitamin C, and Na:K; while protein, fibre, K, Fe and vitamin A decreased. Diets of individuals who ate traditional First Nations food (e.g. wild plants and game animals) on the day of the recall were lower in UPF. CONCLUSIONS: UPF were prevalent in First Nations diets. Efforts to curb UPF consumption and increase intake of traditional First Nations foods and other fresh or minimally processed foods would improve diet quality and health in First Nations peoples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".