Tobacco, alcohol and marijuana use among Indigenous youth attending off-reserve schools in Canada: cross-sectional results from the Canadian Student Tobacco, Alcohol and Drugs Survey
Bibliographic record
Abstract
INTRODUCTION: Ongoing surveillance of youth substance use is essential to quantify harms and to identify populations at higher risk. In the Canadian context, historical and structural injustices make monitoring excess risk among Indigenous youth particularly important. This study updated national prevalence rates of tobacco, alcohol, and marijuana use among Indigenous and non-Indigenous students. METHODS: Differences in tobacco, alcohol, and marijuana use were examined, using logistic regression, among 1700 Indigenous and 22 800 non-Indigenous youth in Grades 9-12 who participated in the 2014/15 Canadian Student Tobacco, Alcohol and Drugs Survey. Differences by sex were also examined. Mean age of first alcohol and marijuana use was compared in the two populations using OLS regression. Results were compared to 2008/09 data. RESULTS: While smoking, alcohol, and marijuana rates have decreased compared to 2008/09 in both populations, the gap between the populations has mostly not. In 2014/15, Indigenous youth had higher odds of smoking (odds ratio [OR]: 5.26; 95% confidence interval [CI]: 3.54-7.81) and past-year drinking (OR: 1.43; 95% CI: 1.16- 1.76) than non-Indigenous youth. More Indigenous than non-Indigenous youth attempted quitting smoking. Non-Indigenous males were less likely to have had at least one drink in the past-year compared to non-Indigenous females. Indigenous males and females had higher odds of past-year marijuana use than non-Indigenous males (OR: 1.84; 95% CI: 1.32-2.56) and females (OR: 2.87; 95% CI: 2.15-3.84). Indigenous youth, especially males, drank alcohol and used marijuana at younger ages. CONCLUSION: Additional policies and programs are required to help Indigenous youth be successful in their attempts to quit smoking, and to address high rates of alcohol and marijuana use.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".