Cannabis Use And Oral Health Among First Nations In Canada
Bibliographic record
Abstract
Canada legalized recreational cannabis in 2018 raising concerns among Indigenous peoples about the impact of cannabis on their oral health. We examined the association between cannabis and self-rated oral health in a national sample of First Nations (FN) youth and adults. Weighted samples representing 45,030 FN youth (12-17 years) and 269,186 FN adults (18 years+) were derived from the 2015-2016 FN Regional Health Survey approved by FN Information Governance Centre and University of Toronto Research Ethics Board. Logistic Regression evaluated fair/poor self-rated oral health. Independent variables included: cannabis use in the past year, socio-demographic characteristics, dental care access, self-reported oral conditions and perceived treatment needs, smoking, alcohol and drug use, diabetes, sugary foods. 27.2% FN youth and 30.3% FN adults reported cannabis use. Among adults, cannabis use was statistically significantly associated with self-rated fair/poor oral health, but when adjusting for other independent variables, only medical cannabis use versus no cannabis use was statistically significant (Adjusted odds ratio=1.37; 95% CI=1.08, 1.74; P<0.05). Among youth, non-medical cannabis use was statistically highly significant with an adjusted odds ratio for fair/poor oral health of 1.73 (95% CI=1.27, 2.35, P=0.001). Statistically significant associations between cannabis use and poor oral health were found in a national FN health survey, forming a baseline to measure trends in cannabis use and its impact on oral health among First Nations peoples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".