Cultural continuity, traditional Indigenous language, and diabetes in Alberta First Nations: a mixed methods study
Bibliographic record
Abstract
INTRODUCTION: We used an exploratory sequential mixed methods approach to study the association between cultural continuity, self-determination, and diabetes prevalence in First Nations in Alberta, Canada. METHODS: We conducted a qualitative description where we interviewed 10 Cree and Blackfoot leaders (members of Chief and Council) from across the province to understand cultural continuity, self-determination, and their relationship to health and diabetes, in the Alberta First Nations context. Based on the qualitative findings, we then conducted a cross-sectional analysis using provincial administrative data and publically available data for 31 First Nations communities to quantitatively examine any relationship between cultural continuity and diabetes prevalence. RESULTS: Cultural continuity, or "being who we are", is foundational to health in successful First Nations. Self-determination, or "being a self-sufficient Nation", stems from cultural continuity and is seriously compromised in today's Alberta Cree and Blackfoot Nations. Unfortunately, First Nations are in a continuous struggle with government policy. The intergenerational effects of colonization continue to impact the culture, which undermines the sense of self-determination, and contributes to diabetes and ill health. Crude diabetes prevalence varied dramatically among First Nations with values as low as 1.2% and as high as 18.3%. Those First Nations that appeared to have more cultural continuity (measured by traditional Indigenous language knowledge) had significantly lower diabetes prevalence after adjustment for socio-economic factors (p =0.007). CONCLUSIONS: First Nations that have been better able to preserve their culture may be relatively protected from diabetes.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".