Non-medical cannabis use among Indigenous Canadians: A systematic review of prevalence and associated factors
Bibliographic record
Abstract
BACKGROUND: Indigenous Canadians may be at an increased risk of non-medical cannabis use. The aim of this review was to synthesize the prevalence of non-medical cannabis use and its associated factors among Indigenous Canadians. METHODS: We systematically searched MEDLINE, EMBASE, Web of Science, and Scopus from inception to January 29th, 2020 for publications reporting the prevalence of non-medical cannabis use among Indigenous Canadians. We included studies published in English after January 1st, 2000. Included publications were hand-searched for potentially relevant peer-reviewed and gray literature publications. Results were synthesized descriptively. RESULTS: We identified 16 peer-reviewed and 7 gray literature publications which met our inclusion criteria. All data were collected prior to cannabis legalization in Canada (October 17th, 2018). The most recent estimates of prevalence of use in the past year were 27% among on-reserve First Nations adults, 50% among off-reserve First Nations adults, and 60% among Nunavik Inuit. In youth, they were 45% among all Indigenous youth grades 9-12, 27% among on-reserve First Nations youth aged 12-17, and 69% in Nunavik Inuit aged 16-22. Direct comparisons indicated a 1.2-15 times higher prevalence of use in Indigenous compared to non-Indigenous youth. Factors associated with cannabis use in adults included younger age and male sex. In youth, factors included older age, poorer mental and physical health, and a poorer relationship with school. CONCLUSION: Results suggest that Indigenous Canadians are at a higher risk for non-medical cannabis use than the general Canadian population. Further research is warranted to inform the development of targeted interventions.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".