Cannabis Users Identifying Themselves as Non-Cigarette Smokers: Who Are They?
Bibliographic record
Abstract
Introduction and Aims. About 20% of cannabis consumers report not smoking cigarettes. Studies that have compared cannabis and cigarette smokers, cigarette smokers, and cannabis users who do not smoke cigarettes (CNSs) have shown that CNSs have better outcomes across a range of indicators compared to the others. Therefore, we conducted a qualitative study to determine why CNSs did not smoke cigarettes and how they managed to resist cigarette smoking in order to better inform prevention efforts. Design and Methods. We conducted five focus groups (FG) with a total of 19 CNSs between ages 16 and 25. A narrative analysis of FGs was conducted using qualitative analysis software. Results. CNSs’ non-smoking choice was rooted in a negative opinion of cigarettes and a harm-reduction strategy. They were unique cases within their peer groups, but there were no CNSs groups. All participants were confronted to the mulling paradox. Discussion and Conclusions. While tobacco-use prevention seems to have been successful, CNSs need to be informed of harmful consequences of chronic cannabis use. Given their habit of adding tobacco to cannabis, CNSs need to be alerted that they may be nicotine dependent even though they do not smoke tobacco on its own. This exploratory study brings essential insight concerning this specific population of cannabis consumers which future research should continue to develop.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".