Gender Influences Self-Reported Use Patterns and Demographics in Canadian Cannabis Users
Bibliographic record
Abstract
With federal legalization of cannabis in Canada and hemp in the USA, there is much interest in consumer demographics and use patterns. The objective of this study was to examine gender differences in cannabis use patterns and demographics in Southwestern Ontario, Canada. A 31 question, online market research survey was conducted on past and present cannabis use from March 2018 to October 2019, including Canadian respondents before and after federal cannabis legalization. The associations between gender and self-reported use reason and frequency, route of administration, cannabinoid, and life stage when starting were assessed. Possible differences were assessed by the Chi Square test and two sample t-test (age only). There were 2264 male and 1830 female respondents to the survey, with an average age of 34 ± 13 years. Across genders, the majority of users first started in high school (59.5%). Gender was associated with frequency of use (P < 0.001), higher proportions of males (73.9%) than females (61.1%) were daily users. Males more often reported using inhalation routes of administration, both vapour (43.6% M vs. 30.7% F) and smoking (85.3% M vs. 77.9% F) (P < 0.001). Interestingly, there were no significant differences in the use of sublingual (13.5% total), oral (47.0%) or suppository (0.6%) products between the groups. A greater proportion of males reported recreational cannabis use (79.6% M vs. 72.7% F) (P < 0.001). This agrees with an association between gender and reason for use for which a greater proportion of males reported using cannabis to socialize/relax (68.5% M vs. 54.8% F) and to receive a high (33.0% M vs. 22.6% F) (P < 0.001). Furthermore, the psychoactive component of cannabis, THC, was the most frequently consumed cannabinoid among a greater proportion of males (38.3%) than females (25.7%). Interestingly, there were equal proportions of males and females using cannabis for digestion, controlling pain, and reducing seizures. There are significant associations between gender and cannabis use in central Canada. Males and females report using cannabis for different reasons and in different ways and frequencies. With further research there is great potential for cannabis in health and wellness and these data are essential components to inform study design and progress this research area forward. KGK Science Inc.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".