“Becoming a Medical Cannabis User” Revisited: Understanding the Role of Low-Threshold Access Points in British Columbia, Canada
Bibliographic record
Abstract
The work of sociologist Howard Becker has been used extensively to study and theorize the experience of using cannabis for recreational purposes and more recently, the experience of using cannabis for medical purposes. Building on recent research that uses Becker's work to study and theorize the process of becoming a medical cannabis use r , we extracted and analyzed interview data from a larger qualitative case study of low-threshold access points (i.e., medical dispensaries and cannabis clubs) in British Columbia (Canada). The majority of participants were 50 years or older, had an annual income of less than $30,000, listed disability assistance as their primary source of income, and were renting a room or an apartment. Educational level was distributed across the sample and gender representation was close to balanced with seven cisgender women and five cisgender men. All of the participants identified as White (European descent). In terms of cannabis consumption, the majority of participants reported using daily and indicated a preference for ingesting cannabis, followed by smoking and applying topically. Our theory-informed analysis suggests that becoming a medical cannabis user is a process that requires low-threshold access to community, medicine, and space—three domains across which learning unfolds, in relationships (with staff and peers) and in response to health and social needs. Our analysis also points to three categories of learning: learning about medical cannabis, learning to medicate, and learning to substitute. Overall, our findings indicate that learning across all three categories and low-threshold access to community, medicine, and space go hand in hand in the process of becoming a medical cannabis user—thus generating new theoretical insights and empirical avenues. The findings also raise important questions about the closure of low-threshold access points in British Columbia and the impact of cannabis legalization more generally.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".