Understanding and Rebalancing: A Rapid Scoping Review of Cannabis Research Among Indigenous People
Bibliographic record
Abstract
Evidence-based perspectives on and patterns of cannabis use are vital to addressing ethical, legal, and regulatory controversies, but have not yet been mapped for Indigenous people. We searched five databases and used a rapid scoping review methodology to analyze empirical studies with a primary focus on cannabis and Indigenous peoples. Studies were examined for year of publication, origin of study and author groups, methods, and thematic foci. We analyzed 68 studies with publication dates between1983 and 2022. Approximately 90% of articles were written by authors in the same geographic location as the study population. Seventy-one percent (71%) of the articles were written by authors of multiple articles. Four articles acknowledged author Indigeneity. None contained author positionality statements. The majority of studies utilized mixed methods that integrated both qualitative and quantitative components. Two major categories of focus that emerged from the analysis are substance use disorders and prevalence rates ( n =35) and predictors of and motivators for use ( n =27), together representing the majority of articles ( n =52/68). Impact on mental health ( n =6), treatment, and management of cannabis use disorder (CUD) ( n =3), legalization and criminalization ( n =2), genomic heritability and dependence ( n =2), and economics of cannabis use ( n =1) were the focus of the remaining articles in the sample. Mixed methods empirical research largely focuses on risks of cannabis use among Indigenous people worldwide. The small, repeating pool of senior authors represents an opportunity for capacity building. A lack of transparency about author positionality and absence of empirical studies that explore the lived experiences of Indigenous peoples and cannabis are significant gaps poised to be filled for future research and regulation.
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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 | 0.097 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.059 | 0.033 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".