Experiences, impacts, and perspectives of recreational cannabis use among Indigenous communities: A scoping review.
Bibliographic record
Abstract
OBJECTIVE: Research on the patterns and correlates of cannabis use suggests a disproportionately higher use incidence across Indigenous populations in Canada and the United States. As cannabis use is related to several deleterious social and behavioral consequences for which Indigenous Peoples are disproportionately vulnerable, it is a public health concern. We gathered and organized the extant research on cannabis use among Indigenous groups to describe emergent themes and future research areas. METHOD: We conducted a scoping review of articles published between 2005 and February 2020 on Indigenous populations and cannabis use in Canada and the United States. We screened 1,934 articles indexed through APA PsycInfo, Scopus, and Medline, 152 of which were systematically coded and analyzed. RESULTS: Most studies focused on the prevalence and patterns of cannabis use initiation, followed by protective and risk factors, correlations of cannabis use and other substance use, correlations of cannabis with mental health diagnoses and with other health consequences, interventions for cannabis use, and cannabis legalization issues. Most articles focused on youth, were analyzed quantitatively, and discussed cannabis use in the context of general substance use. CONCLUSIONS: Future research should explore culturally appropriate intervention programs and the impact of cannabis legalization issues. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".