Cannabis use among a nationally representative cross-sectional sample of smokers and non-smokers in the Netherlands: results from the 2015 ITC Netherlands Gold Magic Survey
Bibliographic record
Abstract
Objectives Existing evidence shows that co-occurring use of tobacco and cannabis is widespread. Patterns of co-use of tobacco and cannabis may change as more jurisdictions legalise medicinal and/or recreational cannabis sales. This analysis examined predictors of current cannabis use and characterised methods of consumption among smokers and non-smokers in a context where cannabis use is legal. Setting The 2015 International Tobacco Control Netherlands—Gold Magic Survey conducted between July and August 2015. Participants Participants (n=1599; 1003 current smokers, 283 former smokers and 390 non-smokers) were asked to report their current (past 30-day) use of cigarettes and cannabis. Cigarette smokers reported whether they primarily used factory made of roll-your-own cigarettes. Those who reported any cannabis use in the last 30 days were asked about forms of cannabis used. X 2 and logistic regression analyses were used to assess relationships among combustible tobacco and cannabis use. Results Past 30-day cannabis use was somewhat higher among current tobacco (or cigarette) smokers (n=57/987=5.8%) than among former or never smokers (n=10/288=3.5% and n=6/316=1.9%, respectively). Joints were the most commonly used form of cannabis use for both current cigarette smokers (96.9%) and non-smokers (76.5%). Among those who smoked cannabis joints, 95% current smokers and 67% of non-smokers reported that they ‘always’ roll cannabis with tobacco. Conclusions In this Netherlands-based sample, most cannabis was reported to be consumed via smoking joints, most often mixed with tobacco. This behaviour may present unique health concerns for non-cigarette smoking cannabis users, since tobacco use could lead to nicotine dependence. Moreover, many non-cigarette smoking cannabis users appear to be misclassified as to their actual tobacco/nicotine exposure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".