International differences in patterns of cannabis use among adult cigarette smokers: Findings from the 2018 ITC Four Country Smoking and Vaping Survey
Bibliographic record
Abstract
BACKGROUND: Although evidence shows that co-use of cigarettes and cannabis is common, there is little research examining if co-use patterns vary depending on the regulatory environment for cannabis. This study examined patterns of co-use and perceptions of relative harm among cigarette smokers in four countries with different histories, and at different stages of cannabis legalization. METHODS: Data are from the 2018 International Tobacco Control 4CV Survey and included 10035 adult cigarette smokers from Canada, United States (US), Australia, and England. At the time of the survey, Canada and the US had relatively more permissive cannabis regulations compared to Australia and England. RESULTS: Among this sample of 10035 cigarette smokers, Canada had the highest rate of cannabis co-use in the last 12 months (36.3%), followed by the US (29.1%), England (21.6%), and Australia (21.4%). Among past 12 month co-users (n = 3134), the US (40.2%) and Canada (35.2%) had the highest rates of daily cannabis use, followed by smokers in England (26.3%) and Australia (21.7%); Australian co-users had the highest rate of infrequent (<monthly) cannabis use. The highest proportion of co-users who smoked daily and used cannabis daily was in the US (34.8%), followed by Canada (30.6%), England (25.8%), and Australia (22.7%). More co-users in the US (78.3%) and Canada (73.6%) perceived smoked cannabis to be less harmful than cigarettes than in Australia (65.5%) and England (60.8%). The majority of co-users who used cannabis in the last 30 days had smoked it (92.3%), with those in England more likely to smoke cannabis (95.7%) compared to Canada (88.6%); there were no other differences between countries (US: 92.0%, Australia: 93.0%). Co-users in England (90.4%) and Australia (86.0%) were more likely to mix tobacco with cannabis than co-users in Canada (38.5%) and the US (22.3%). CONCLUSION: Patterns of tobacco and cannabis co-use differed between countries. Smokers in Canada and the US had higher rates of co-use, daily cannabis use, dual-daily use of both cannabis and cigarettes, and were more likely to perceive smoked cannabis as less harmful than cigarettes compared to England and Australia. Further attention as to how varying cannabis regulations may impact co-use patterns is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".