Risky cannabis use is associated with varying modes of cannabis consumption: Gender differences among Canadian high school students
Bibliographic record
Abstract
Background: Our objective was to explore associations between indicators of more risky cannabis use (i.e., solitary use, frequent use, and younger age of initiation) and different modes of cannabis use (i.e., smoking, vaping and/or edibles). Methods: = 4,763). Generalized estimating equations were used to examine associations between risky cannabis use and modes of cannabis use, stratified by gender. Results: Overall, 38% of students reported using multiple modes of cannabis use. Consistent among both males and females, students who used cannabis alone (35%) and at a higher frequency (55%) were more likely to use multiple modes than smoking only. Among females, those who used cannabis alone were more likely to report using edibles only compared to smoking only (aOR=2.27, 95%CI=1.29-3.98). Earlier cannabis use initiation was associated with lower likelihood of vaping cannabis only among males (aOR=0.25; 95%CI = 0.12-0.51), and lower likelihood of using edibles only among females (aOR=0.35; 95%CI = 0.13-0.95), than by smoking only. Conclusions: Our findings suggest that multiple modes of use may be an important indicator or risky cannabis use among youth, given associations with frequency, solitary use, and age of onset.
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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".