Associations of Indigenous language knowledge and physical, emotional, mental, and spiritual balance among First Nations living on reserve in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: A First Nations perspective on wellness includes physical, mental, emotional, and spiritual balance. Indigenous languages hold cultural knowledge and values that could promote wellness. Language learning is one way that Indigenous peoples may reclaim their cultural identity. We theorize that Indigenous language knowledge is one of multiple cultural activities causally downstream from Indigenous reclamation of culture among other causal precursors. METHODS: Our analysis was informed by the results of qualitative interviews with ten Indigenous language learners. We conducted cross-sectional analysis of the First Nations Regional Health Survey (2015-2017) from adults living on First Nations reserves in British Columbia, Canada. Using logistic regression with adjustment for confounding, we estimated associations of Indigenous language knowledge with self-reported physical, mental, emotional, and spiritual balance. RESULTS: In models adjusted for age and sex and compared to those with little or no fluency, among those with intermediate or fluent Indigenous language ability, the odds ratios (95% CI) of being in balance most or all of the time were 1.06 (0.79, 1.42) for physical balance, 1.23 (0.93, 1.62) for mental balance, 1.19 (0.90, 1.58) for emotional balance, and 1.57 (1.18, 2.10) for spiritual balance. In models adjusted for age, sex, and multiple cultural activities, these were 0.94 (0.69, 1.28); 1.05 (0.79, 1.41); 0.99 (0.73, 1.33); and 1.13 (0.82, 1.55) respectively. CONCLUSION: In age/sex-adjusted models, Indigenous language knowledge acted as a proxy for multiple cultural activities theoretically downstream from reclamation and promoters of cultural wellness. Our results are consistent with First Nations cultural activities promoting spiritual balance in this population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".