Comparison of measures of diet quality using 24-hour recall data of First Nations adults living on reserves in Canada
Bibliographic record
Abstract
OBJECTIVE: Assess the diet quality of First Nations adults in Canada using percentage energy from traditional foods (TF) and ultra-processed products (UPP), food portions from the 2007 Eating Well with Canada's Food Guide - First Nations, Inuit and Métis (EWCFG-FNIM) and a Healthy Eating Index (HEI). METHODS: Data collection for this participatory research occurred in 92 First Nations reserves across Canada from 2008 to 2016. Percent daily energy intakes were estimated from 24-hour recalls for TF and NOVA food categories. Portions of food groups from the 2007 EWCFG-FNIM were compared to recommendations. A Canadian-adapted HEI was calculated for each participant. RESULTS: The percent energy from TF was 3% for all participants and 18% for consumers. Meat and alternatives were above the EWCFG-FNIM recommendations and all other food groups were below these. HEI was "low" with only older individuals attaining "average" scores. HEI was above "average" in 4 regions. UPP represented 55% of energy, the largest proportion from a NOVA category. CONCLUSION: The diet quality of First Nations adults in Canada is nutritionally poor. The nutrition, food security and health of First Nations would be improved by better access to TF and healthy store-bought food. However, poor diet is only one aspect of the difficulties facing First Nations in Canada. Researchers and policy makers must strive to better understand the multiple challenges facing First Nations Peoples in order to foster empowerment and self-determination to develop First Nations living conditions and lifestyles that are more culturally sound and more conducive to health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.002 | 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".