Cannabis use prior to legalisation among alcohol consumers in the Canadian Yukon and Northwest territories
Bibliographic record
Abstract
Although rates of substance use are higher in the Canadian territories than the provinces, there is little research on cannabis use. This exploratory study describes cannabis use and related risk behaviours among alcohol consumers in Whitehorse (Yukon) and Yellowknife (Northwest Territories), with comparisons to data from the provinces. Prior to non-medical cannabis legalisation, respondents (n = 387) aged ≥19 were recruited from a study on alcohol labelling to complete an online cannabis survey. Logistic regression was used to compare territorial and provincial data, and correlates of cannabis use in the territories. Forty-seven percent of respondents were past 12-month cannabis consumers, and 15.5% were daily/almost daily consumers, significantly higher than in the provinces (p < 0.001 for both). Dried herb (85.7%) and edibles (58.2%) were most commonly used among consumers. Use of dried herb, edibles, solid concentrates and tinctures was significantly higher than in the provinces (all p ≤ 0.01). Twenty-four percent of respondents had ridden with a driver who had used cannabis, while 31.9% of cannabis consumers had driven within 2h of cannabis use, significantly higher than the provinces (both p < 0.001). Further research should examine the impact of legalisation on cannabis use in the territories, including rural communities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".