Patterns of problematic cannabis use in Canada pre‐ and post‐legalisation: Differences by neighbourhood deprivation, individual socioeconomic factors and race/ethnicity
Bibliographic record
Abstract
INTRODUCTION: The legalisation of cannabis in Canada in 2018, and subsequent increase in prevalence of use, has generated interest in understanding potential changes in problematic patterns of use, including by socio-demographic factors such as race/ethnicity and neighbourhood deprivation level. METHODS: This study used repeat cross-sectional data from three waves of the International Cannabis Policy Study web-based survey. Data were collected from respondents aged 16-65 prior to cannabis legalisation in 2018 (n = 8704), and post-legalisation in 2019 (n = 12,236) and 2020 (n = 12,815). Respondents' postal codes were linked to the INSPQ neighbourhood deprivation index. Multinomial regression models examined differences in problematic use by socio-demographic and socio-economic factors and over time. RESULTS: No evidence of a change in the proportion of those aged 16-65 in Canada whose cannabis use would be classified as 'high risk' was noted from before cannabis legalisation (2018 = 1.5%) to 12 or 24 months after legalisation (2019 = 1.5%, 2020 = 1.6%; F = 0.17, p = 0.96). Problematic use differed by socio-demographic factors. For example, consumers from the most materially deprived neighbourhoods were more likely to experience 'moderate' vs 'low risk' compared to those living outside deprived neighbourhoods (p < 0.01 for all). Results were mixed for race/ethnicity and comparisons for high risk were limited by small sample sizes for some groups. Differences across subgroups were consistent from 2018 to 2020. DISCUSSION AND CONCLUSIONS: The risk of problematic cannabis use does not appear to have increased in the 2 years following cannabis legalisation in Canada. Disparities in problematic use persisted, with some racial minority and marginalised groups experiencing higher risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".