The impact of legalization of access to recreational Cannabis on Canadian medical users with Cancer
Bibliographic record
Abstract
BACKGROUND: Canada legalized cannabis use for medical purposes in 1999. Legalization of cannabis for recreational purposes in October 2018 offered the opportunity to assess the impact of recreational legalization on cancer patients' patterns of use to identify learning points that could be helpful to other countries considering similar legislation. METHOD: Two identical anonymous cross-sectional surveys were administered to cancer patients in British Columbia 2 months before and 3 months following legalization, with the same eligibility criteria. The prevalence of medical cannabis use, the distribution of symptoms leading to use, the most common types of cannabis products and sources, reasons for stopping using cannabis, and barriers to access were assessed. RESULTS: The overall response rate was 27%. Both cohorts were similar regarding age (median = 66 yrs), gender (53% female), and education (approximately 85% of participants had an education level of high school graduation and higher). Respondents had multiple motives for taking cannabis, including to manage multiple symptoms, to treat cancer, and for recreational reasons. The majority of patients in both surveys did not use the legal medical access system. Comparison of the two cohorts showed that after legalization the prevalence of current cannabis use increased by 26% (23·1% to 29·1%, p-value 0·01), including an increased disclosure of recreational motive for use, from 32 to 40%. However, in the post-legalization cohort more Current Users reported problems getting cannabis (18%) than the pre-legalization cohort (8%), (p-value < 0·01). The most common barrier cited was lack of available preferred products, including edibles, as these were only available from illegal dispensaries. CONCLUSIONS: Results showed that legalization of cannabis for recreational purposes may have an impact on those who use medical cannabis. Impacts include an increase in prevalence of use; problems accessing preferred products legally; higher cost, and difficulties using a legal access system. The desired goal of regulation in reducing harms from use of illegal cannabis products are unlikely to be achieved if the legal process is less attractive to patients than use of illegal sources.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".