Eating traditional foods enhances diet quality among First Nations in Canada: an analysis using the Healthy Eating Food Index-2019 (HEFI-2019) and the Canadian Healthy Eating Index 2007 (C-HEI 2007)
Bibliographic record
Abstract
Understanding the dietary patterns of First Nations is crucial for addressing health disparities and promoting well-being. Historical assaults (colonization and loss of control over their lands) have strongly altered dietary practices and impacted health outcomes for generations. Canada conducts regular surveys to assess the extent to which individuals adhere to dietary guidelines. However, Indigenous peoples living on reserves are excluded from these surveys. This study aims to assess the diet quality of First Nations adults using the Healthy Eating Food Index-2019 (HEFI-2019) and the Canadian Healthy Eating Index 2007 (C-HEI 2007) and identify their influencing factors. Data were collected from adults (19 years and older) across ninety-two First Nations communities throughout Canada. Participants provided information on sociodemographic factors and dietary intake using structured questionnaires and 24 h dietary recalls. Statistical analyses included mean scores and regression models to assess associations between dietary indices and influencing factors. The mean HEFI-2019 and C-HEI 2007 scores among First Nations adults were 35/80 and 49/100, respectively, indicating suboptimal adherence to dietary guidelines compared to the Canadian population. Factors such as region, age, sex, education level, number of working people in the household, smoking status, and traditional food intake significantly influenced diet quality. This study underscores the importance of understanding and improving the diet quality of First Nations adults as measured by HEFI-2019 and C-HEI 2007 scores. While acknowledging the low adherence to dietary guidelines, particularly in younger age groups, the study highlights the positive influence of traditional foods on diet quality within Indigenous communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".