Conceptualizing ‘cannabis harm reduction’: lessons learned from cannabis compassion clubs and medical dispensaries in British Columbia (Canada)
Bibliographic record
Abstract
OBJECTIVES: Drawing on a qualitative case study of cannabis compassion clubs and medical dispensaries in British Columbia (Canada), the main goal of this paper is to generate insights that have the potential to advance and broaden the conceptualization of 'cannabis harm reduction'. METHODS: We undertook a qualitative case study by drawing on seven data sources: (1) online content, (2) news stories, (3) legal documents, (4) policy documents, (5) information about enforcement, (6) interviews with (i) key informants, (ii) participants with operational experience (i.e., people engaged in the active operations of compassion clubs/dispensaries in various roles), and (iii) participants with lived experience of medicating with cannabis, and finally (7) field notes. For this paper, we applied a harm reduction lens to the participant interview data. RESULTS: Applying a harm reduction lens to the participant interview data allowed us to identify two main conceptual dimensions: structural and operational. The structural dimension focused on the work undertaken by cannabis compassion clubs and medical dispensaries to address a risk environment created by systems, laws, and policies. The main themes identified here were access, safety, and quality. The operational dimension focused on the characteristics of the services provided cannabis compassion clubs and medical dispensaries. The main themes identified here were low-threshold, compassion, and supports. Our findings suggest that these two dimensions worked together to generate conditions conducive to 'cannabis harm reduction'. CONCLUSIONS: Based on our findings, we identified research, policy, and advocacy implications. We argue that research should focus on loss of access, regulation, a broader conceptualization of cannabis substitution, and better integration between cannabis and harm reduction. We also highlight the need for a harm reduction analysis of the Cannabis Act, new community-oriented models to meet the needs of people who medicate with cannabis, and non-profit supply pathways. Finally, we suggest that structurally-oriented advocacy is needed to achieve community-oriented models of cannabis cultivation, distribution, and consumption and that this advocacy would benefit harm reduction more broadly.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.050 | 0.063 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".