Characterizing First Nations’ Traditional Food Environments Across Canadian Ecozones: A Latent Class Analysis
Bibliographic record
Abstract
Traditional foods remain crucial to First Nations’ health and wellbeing and contribute to cultural identity, diet quality, and nutrition-related disease incidence. Many policies in Canada intentionally impacted the intergenerational transmission of First Nations’ culture, profoundly affecting their traditional food environments. Therefore, understanding contemporary traditional food environments - collective physical, social, economic, cultural and political factors shaping dietary choices and nutritional status within a community - is essential for improving nutrition and population health. We aimed to identify and describe First Nations’ traditional food environments and their associations with individual participants’ demographics and health characteristics. Traditional food environments were characterized using data from the First Nations Food, Nutrition, and Environment Study (FNFNES) (N=6487, 92 communities across Canada). Traditional food items (k=226) from the FNFNES Food Frequency Questionnaire were grouped into nine categories based on an Indigenous conceptual framework. Exploratory Latent Class Analysis (LCA) was conducted with six traditional food categories, using model fit statistics to determine the optimal number of classes (e.g., AIC, BIC, LMR, BLRT), which was then used in a Latent Class Analysis model with all nine Traditional Food categories. Exploratory Latent Class Analysis determined that five classes (patterns) were optimal for the full Latent Class Analysis using model fit statistics. Lastly, the patterns were characterized using means and proportions of sociodemographic and health variables (e.g., anthropometry, physical activity and food security status), with ANOVA and chi-squared tests to assess significant differences. The five patterns describing First Nations’ traditional food environments and their % population distributions within the study sample were “robust traditional food environment” (14.27%), “four sisters traditional food environment” (6.21%), “rich traditional protein environment” (26.74%), “fish, animals and fruits traditional food environment” (35.30%) and “limited access to traditional food environment” (17.46%). This research is the first to wholistically describe First Nations’ traditional food environments based on individual consumption patterns in Canada. It is also the first to use an Indigenous conceptual framework to categorize Traditional Foods. This research can inform future interventions to enhance food sovereignty efforts and promote greater traditional food access and consumption, aligning with First Nations' preferences
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".