Reasonable access: important characteristics and perceived quality of legal and illegal sources of cannabis for medical purposes in Canada
Bibliographic record
Abstract
BACKGROUND: Throughout the past two decades of legal medical cannabis in Canada, individuals have experienced challenges related to accessing legal sources of cannabis for medical purposes. The objective of our study was to examine the sources of cannabis accessed by individuals authorized to use medical cannabis and to identify possible reasons for their use of illegal sources. METHODS: Individuals who participated in the Cannabis Access Regulations Study (CANARY), a national cross-sectional survey launched in 2014, and indicated they were currently authorized to use cannabis for medical purposes in Canada were included in this study. We assessed differences between participants accessing cannabis from only legal sources versus from illegal sources in relation to sociodemographic characteristics, health-related factors, and characteristics of medical cannabis they considered important. A secondary analysis assessed differences in satisfaction with various dimensions of cannabis products and services provided by legal versus illegal sources. RESULTS: Half of the 237 study participants accessed cannabis from illegal sources. Individuals accessing cannabis from illegal sources were significantly more likely to value pesticide-free products, access to a variety of strains, ability to select strain and dosage, ability to observe and smell cannabis, availability in a dispensary, and availability in small quantities than did individuals accessing cannabis from only legal sources (all p < 0.05). Additionally, participants gave significantly higher satisfaction scores to illegal sources than to legal sources on service-related dimensions of cannabis access (all p < 0.05). CONCLUSION: Our findings contribute to an understanding of reasonable access to medical cannabis from a patient perspective and how to assess whether it has been achieved. Characteristics of cannabis products and services valued by patients and appropriate to their needs should be incorporated into legal medical cannabis programs to promote the use of legal medical sources. While pertaining specifically to medical use of cannabis in Canada, the findings of this study may also be instructive for understanding the use of illegal cannabis sources for non-medical purposes in Canada and provide insight for other jurisdictions implementing cannabis regulations for both medical and non-medical purposes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".