Association Between Markers of Vulnerability for Cannabis-Related Harms and Source of Supply: Secondary Analysis of a Representative Population Survey
Bibliographic record
Abstract
OBJECTIVE: In 2018, the sale of non-medical cannabis was authorized in the province of Quebec in Canada, within a public monopoly under the Société Québécoise du Cannabis (SQDC). The objective of this study was to offer a description of the cannabis-using population regarding the sources of cannabis supply and to explore whether at-risk individuals are purchasing cannabis at SQDC. METHOD: , which was completed between February and June 2019. Analyses involved adjusted binary logistic regressions, incorporating population weights, to assess 7 potential indicators of harm. RESULTS: The vulnerability profiles of SQDC consumers (47.8%) and those acquiring their cannabis elsewhere (52.2%) were similar in terms of frequency of cannabis use (adjusted odds ratio [aOR] = 0.46; 95% confidence interval [CI] = 0.12-1.67), motivation to use (aOR = 0.62; 95% CI = 0.16-2.46), concomitant consumption of other substances (aOR = 0.80; 95% CI = 0.14-4.75), cannabis-impaired driving behaviours (aOR = 0.93; 95% CI = 0.26-3.36), psychological distress (aOR = 0.99; 95% CI = 0.26-3.79), and problematic cannabis use (aOR = 0.46; 95% CI = 0.13-1.64). However, SQDC consumers were more likely to be aware of the cannabinoid content of the product purchased compared to those who acquired their cannabis from other sources (aOR = 4.12; 95% CI = 1.10-15.40). CONCLUSIONS: No association was detected between the source of cannabis supply and potential vulnerability indicators of cannabis-related harms, but SQDC consumers were more aware of the cannabinoid content of the products purchased. These results suggest that the regulated government supply in Quebec is reaching a substantial portion of those with potential high vulnerability to harm. Whether this knowledge translates into a reduction in the negative consequences related to consumption is still to be determined.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".