“Where does the high road lead?” Potential implications of cannabis legalization for pediatric injuries in Canada
Bibliographic record
Abstract
The purpose of this commentary is to discuss how legalization of non-medical marijuana (LNMM) in Canada can potentially influence child and adolescent unintentional injuries based on evidence from states (American) and jurisdictions that have already legalized cannabis for recreational purposes. Although the evidence is still not conclusive, LNMM can bring about higher exposure, lower perceived harms, and higher prevalence of cannabis use by minors through role modeling and normalization of behaviour within the household and the community, and higher rates of driving under the influence of cannabis, which can contribute to a higher burden of road traffic injuries. Experience of American states with LNMM shows higher rates of emergency visits for pediatric poisoning due to unintentional ingestion of cannabis-containing foods and severe burns due to explosions during the course of home-based cannabis extraction. While the justification for legalization has created a strict legal framework for improved control of cannabis in Canada, the implications for health and safety of children and adolescents necessitate further study, communication with policy-makers and public health practitioners, and evidence-based education of parents, caregivers, and youth.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".