Cannabis use and patterns among middle and older aged Canadians prior to legalization: a sex-specific analysis of the Canadian Tobacco, Alcohol and Drugs Survey
Bibliographic record
Abstract
BACKGROUND: The recreational use of cannabis was legalized across Canada in October 2018. While many people use cannabis without harm, adverse outcomes have been noted in a few populations, including middle-aged and older adults. Given that the current literature has neglected to study cannabis use among this population and between sexes, the objective of our study was to identify the prevalence, characteristics, and patterns of cannabis use among middle and older aged males and females prior to legalization in Canada. METHODS: Secondary analysis was conducted on the Canadian Tobacco, Alcohol and Drugs Survey 2017, with the sample restricted to adults ages 40 and above. The main outcome was defined as past-year cannabis use and statistical analysis was conducted separately for males and females. Bivariate and multivariable logistic regression was performed to identify associations between the main outcome and various sociodemographic, health, and substance use variables. Explanatory supplementary variables were also explored. RESULTS: In 2017, 5.9% of females and 9.0% of males over the age of 40 reported past-year cannabis use. Almost 62% of males who used cannabis in the past-year reported a failed attempt at reducing or stopping their cannabis use. Over half (56%) of older females, self-reported using cannabis for medical purposes. Additionally, over one in five older adults reported using a vaporizer or e-cigarette as a delivery method for cannabis. Significant characteristics of male cannabis use included having no marital partner, cigarette smoking, and illegal drug use. Furthermore, significant predictors of past-year cannabis use in females included residing in an urban community, Eastern- Atlantic provinces or British Columbia, having fair/poor mental health, smoking cigarettes, use of other tobacco products, and illegal drugs. CONCLUSION: To our such knowledge, this is the first nationally representative study to outline the prevalence, characteristics, and patterns of past-year cannabis use prior to Canadian legalization, among middle and older aged Canadians. Results from this study are expected to be used to reliably to track changes in usage, behaviours, and related disorders in the years to come.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".