Prevalence and correlates of non-medical only compared to self-defined medical and non-medical cannabis use, Canada, 2015.
Bibliographic record
Abstract
BACKGROUND: The Canadian federal government has committed to legalizing non-medical cannabis use by adults in 2018. Medical use was legalized in 2001; however, not all people reporting medical use have medical authorization. To prepare for monitoring the effects of the policy change, a greater understanding of the prevalence of cannabis use and the characteristics of all cannabis users is needed. DATA AND METHODS: Data from the 2015 Canadian Tobacco, Alcohol and Drugs Survey (CTADS) were used to estimate prevalence and examine reasons for medical use and factors associated with people who reported using cannabis Non-Medically Only (NMO), compared with people who reported Self-Defined Medical and Non-Medical use (SDMNM), including use of other drugs and the non-therapeutic use of psychoactive pharmaceuticals. RESULTS: In 2015, 9.5% of Canadians aged 15 and older reported NMO cannabis use, while another 2.8% reported SDMNM use. Half of Canadians reporting some self-defined medical use cited pain as the primary reason. Daily and near-daily use was significantly more common among SDMNM users (47.2%) than among individuals considered NMO users (26.4%). Past-year cannabis users of any type were more likely to be male and younger, to have used other illicit drugs and at least one of three classes of psychoactive pharmaceutical drugs non-therapeutically, and to be daily smokers or heavy drinkers. SDMNM cannabis use was more common among people reporting worse health (general and mental), use of psychoactive pharmaceuticals, and living in lower-income households. DISCUSSION: Because non-medical cannabis use is common to both user groups analyzed, many similarities were anticipated. Nevertheless, SDMNM users also had several unique characteristics consistent with use to address medical problems. However, because the CTADS does not collect information about whether the individual has received a health care practitioner's authorization to use cannabis for a medical purpose this analysis should not be interpreted as an evaluation of people who access cannabis through Health Canada's medical access program, the Access Cannabis for Medical Purposes Regulations (ACMPR).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 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".