The Impact of Legalization of Access to Recreational Cannabis on Canadian Medical Users with Cancer.
Bibliographic record
Abstract
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 would be of use to other countries considering similar legislation. Method Two identical anonymous cross-sectional surveys were administered to cancer patients in British Columbia, 5 months apart (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% with 821 and 852 individuals returning the first and second surveys respectively. Both cohorts were similar regarding participants’ characteristics, including age (median= 66yrs), gender (53% of participants were female), and education (one-third of participants had an education level of high school or less). Comparison of the two cohorts showed that legalization increased the prevalence of current cannabis use by 21% (23·1% to 29·1%, p-value 0·01). However, after legalization, Current Users reported more issues in getting cannabis (18% compared to 8%, p-value: <0·01). The most common barrier cited was lack of available preferred products, from closure of illegal dispensaries. Conclusions Results showed that legalization of access to cannabis for recreational purposes will have an unintended negative impact on those who use cannabis products for medical purposes. These should be anticipated and mitigated in the design and implementation of new legislation. Keywords Cannabis, Cancer, Survey, Symptom Management
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".