Shifts in medical cannabis use in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has had widespread secondary negative health impacts including loss of material security and exacerbation of mental illness in at-risk populations. While increases in the nonmedical use of certain substances, including cannabis, have been observed in samples of the Canadian population, no research has documented COVID-concurrent shifts in medical cannabis use in Canada. METHODS: Data were derived from the 2021 Canadian Cannabis Patient Survey, an online survey administered in May 2021 to people authorized to use medical cannabis recruited from one of two Canadian licensed medical cannabis producers. McNemar tests assessed for changes in past 3-month medical cannabis frequency from before to during the pandemic. We explored correlates of increasing frequency of cannabis use since before the pandemic in bivariable and multivariable logistic models. RESULTS: In total, 2697 respondents (49.1% women) completed the survey. Daily medical cannabis use increased slightly but significantly from before the pandemic (83.2%) to during the pandemic (90.3% at time of survey; p < 0.001). Factors significantly associated with increasing frequency of medical cannabis use included female gender, younger age, pandemic-related job loss, primary cannabis use to manage mental health, prescription drug use and nonmedical cannabis use (p < 0.05). CONCLUSION: There were slight shifts towards higher frequency of medical cannabis use after the onset of the COVID-19 pandemic. While short- and long-term impacts of cannabis use on pandemic-related mental distress are unknown, clinicians working with patients who use medical cannabis should be aware of possible changes in use patterns during the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".