Traditional Languages, Cultural Practices and the Oral Health of First Nations Children: A Strength-Based Approach.
Bibliographic record
Abstract
First Nations children in Canada continue to experience oral health and nutritional inequities.The objective of this cross-sectional study was to determine the statistical relationship between traditional languages, cultural practices, and untreated dental caries and malnutrition risk in First Nations children in Northern Ontario and Manitoba. Nested in the Nishtam Niwiipitan (My First Teeth) Study, predictor variables measured speaking traditional languages and cultural practices of caregivers. Outcome variables measured untreated dental caries and malnutrition risk in children. A child’s odds (adjusted for select covariates) of having 3 or more untreated decayed teeth are 59% less (OR 0.41, 95% CI: 0.2 - 0.9, p=.02) if the caregiver speaks a First Nations language versus English daily. A child’s odds of having high malnutrition risk are 40% less if the caregiver speaks a First Nations language versus English daily (OR 0.6, 95% CI: 0.3 - 1.3), however this relationship was not statistically significant. Traditional languages may be harnessed as Indigenous-led, strength-based approaches to oral health programming with First Nations children.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".