Association between supply source and vulnerability markers to cannabis-related harms: a cross-sectional study
Bibliographic record
Abstract
Following cannabis legalization, some Canadian provinces created government-operated stores (e.g. Société québécoise du cannabis, SQDC) to distribute and sale cannabis and protect public health. However, little information exists on cannabis-related harms as a function of supply source. This study analyses data from the Quebec cannabis survey conducted between February 11th and June 9th 2019 in 1836 adult (>18 years old) cannabis users. Binary logistic regressions were used to evaluate the association between seven vulnerability markers to cannabis-related harms and supply source. Individuals who bought their cannabis at the SQDC (46%) had similar profile compared with individuals who bought their cannabis elsewhere (54%) in terms of psychological distress (adjusted odds ratio [aOR]=1.0; 95% confidence interval [CI]=0.3-3.8, p=0.991), risky motor driving (aOR=0.9, 95% CI=0.3-3.4, p=0.913), other substance co-use (aOR=0.8, 95% CI=0.2-3.9, p=0.765), problematic cannabis use (aOR=0.5, 95% CI=0.1-1.6, p=0.230), use to deal with negative affect (aOR=0.6, 95% CI=0.2-2.5, p=0.499) and cannabis use frequency (aOR=0.5, 95% CI=0.1-1.7, p=0.238). However, individuals who did not bought their cannabis at the SQDC had increased odds of ignoring the cannabinoid content of their cannabis product compared with those who bought their cannabis from SQDC (aOR=4.1, 95% CI=1.1-15.4, p=0.035). This result is coherent with SQDC's objective to inform cannabis users on their products, including the content and ratio of cannabinoids. More research is needed to see if this lower vulnerability marker translates into fewer negative consequences from cannabis use. Poster presented at the 22nd congress of students, fellows and residents of the Research Centre of the Centre hospitalier de l'Université de Montréal, Montreal (Canada), May 5-6 2021.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".