Conversations on Cannabis and Mental Health: Recommendations for Health and Social Care Providers from Indigenous 2SLGTBQQIA+ People in Canada
Bibliographic record
Abstract
With the recent legalization of cannabis in Canada, there is an urgent need for information about its effects on Indigenous populations due to the impact of cannabis on the mental health of Indigenous Peoples in Canada being largely unknown. Using the guiding principles of Etuaptmumk (Two-Eyed Seeing), Sharing Circles were held to hear the needs and experiences of Indigenous People in relation to their mental health and cannabis use. From these engagements and using gender-based and distinctions-based analysis, four recommendations were developed for academic institutions, medical regulatory authorities and health and social care providers (HSCPs) to consider when caring for Indigenous People living with mental health issues. The findings point to the disconnection between recent research on medical cannabis and its availability to Indigenous People through accessible mediums, HSCPs, and the lack of cultural safety in health and social services. The four recommendations provided are helpful to both educate frontline HSCPs about the needs and experiences of Indigenous People and improve access to current information and best practices for Indigenous People who use cannabis for mental health from the regulatory and representation perspective.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.046 | 0.013 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".