Impact of legalization on cannabis exposure calls to the British Columbia Poison Control Centre
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine whether cannabis exposure calls to the British Columbia Drug and Poison Information Centre (DPIC) were impacted by the legalization of non-medical cannabis in Canada. METHODS: We fit interrupted time series models to monthly counts of cannabis cases from 2013 to 2021, stratified by age and cannabis form. We set the intervention month to October 2018 legalization for cases involving inhaled dried cannabis and ingestible oils and capsules. We set the intervention month to January 2020 for cases involving edibles and inhaled concentrates to reflect their commercial rollout after their October 2019 legalization. RESULTS: DPIC managed 3989 cases involving cannabis exposure between 2013 and 2021. The rate (95% CI) of all cannabis cases increased by 17% (14%, 20%) annually from 2013 to October 2018 legalization. The highest pre-legalization increase was in pediatric edible cases with 52% (36%, 69%) and 57% (35%, 82%) annual increases among children aged 5 and under and 6 to 12, respectively. Upon legalization, the rate of cases consuming oil and capsule products spiked by 26% (- 19%, 96%) followed by a decrease, but remaining higher than the pre-legalization rate. Legalization did not have an immediate effect on the rate of cases involving edibles or inhaled cannabis, which all continued to increase post-legalization, albeit at slower rates. CONCLUSION: Regardless of the contributing factors to cannabis case trends at DPIC, these data highlight the importance of poisoning prevention policies, promotion of low-risk use, and routine surveillance.
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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 | 0.002 | 0.022 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".