Outdated tools, underestimated harm: Modernizing cannabis surveillance in a post‐legalization era
Bibliographic record
Abstract
BACKGROUND: Canada's 2018 legalization of non-medical cannabis was positioned as a public health initiative, intended to shift cannabis use from criminalization to regulation. Since then, cannabis access and consumption have grown significantly but the systems used to monitor cannabis-related harms have not kept pace. Most national surveys remain focused on patterns of use rather than indicators of harm. Cannabis use disorder (CUD), a clinical condition with well-established diagnostic criteria, remains rarely measured, often misclassified, and largely absent from policy discussions. ARGUMENT: Canada's current surveillance tools are not equipped to detect or track CUD. Major national surveys rely on outdated DSM-IV frameworks, use skip logic that excludes individuals with lower levels of use, and fail to assess key symptoms such as loss of control, functional impairment, or withdrawal. The primary mental health surveillance tool does not align with DSM-5 standards. Administrative health records often obscure CUD due to underdiagnosis, inconsistent coding practices, and the absence of validated case-finding tools. High-risk populations are often excluded from survey samples. As a result, CUD is underdetected, undertreated, and underfunded across Canada's healthcare and policy systems. CONCLUSION: Canada urgently needs a modernized surveillance infrastructure that reflects current diagnostic standards, improves the sensitivity of survey tools, incorporates equity metrics, and enables the reliable detection of cannabis use disorder in clinical data.
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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 | 0.054 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".