Oral health in a First Nations and a non-Aboriginal population in Manitoba
Bibliographic record
Abstract
OBJECTIVES: To analyze the prevalence of poor oral health and selected determinants in First Nations (FN) and Caucasian samples in Manitoba, Canada. STUDY DESIGN: Cross-sectional survey, nested in a cohort study. METHODS: FN and Caucasian participants completed a questionnaire on socio-demographic variables, oral health symptoms, and oral health-related behaviours as part of a broader cohort study comparing these ethnic groups for different chronic immune mediated diseases. RESULTS: Caucasians reported higher levels of employment, education, and urban dwelling than FNs (p<0.001). FNs reported smoking more, and having poorer oral health-related behaviours than Caucasians (p<0.001). After adjustment for age and sex, FN reported having more oral health symptoms than Caucasians (odds ratio (OR): 2.71; 95% confidence interval (CI): 1.73, 4.52), but the association was reduced and not statistically significant after adjustment for other socio-demographic variables (OR=1.34; 95% CI: 0.58, 3.10). Oral health symptoms were associated with current smoking among FN (adjusted OR=2.67, 95% CI: 1.05, 6.78). Oral hygiene behaviours were significantly related to smoking status, rural living and education for both groups. CONCLUSIONS: Oral health-related behaviours and smoking were found to be significant factors explaining poor oral health, which were lower for the FNs cohort than the Caucasian sample. However oral health and related behaviours were less related to their ethnicity than to socio-demographic factors, suggesting that policies to change behaviour will not result in lasting reductions in oral health differences between these groups in Manitoba.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".