Impact of Legalized Recreational Cannabis on Emergency Department Presentations: A Systematic Review
Bibliographic record
Abstract
Background: The advent of recreational cannabis legalization (RCL) has altered public health circumstances globally, and as cannabis and cannabis-related harms are measured, the impact on emergency department (ED) presentations has received growing attention. Countries such as Canada and Uruguay, and jurisdictions including several States in the United States, the European Union, and Oceania have recently adopted RCL; therefore, further understanding of the implications for the healthcare system is warranted. Aim: The objectives of this review are to summarize evidence from 2016 to 2024 about the global impact of RCL on ED presentations, specifically cannabis-related harms, as well as specific population and socio-demographic differences. Methods: A systematic review was conducted in PubMed, EMBASE, PsycINFO, and Web of Science with searches up to November 2024. Of these, sixty studies were peer-reviewed and had longitudinal, cohort, or time-series designs, which resulted in quantitative data collected over the designated review time period. Extracted data included ED visit rates, demographics, clinical outcomes, and health care costs from various regions worldwide. Results: Increased ED visits for cannabis-related presentation due to RCL was reported based on available evidence, including a 2.6-fold increase in ED visits for pediatric poisonings with cannabis, an increase in presentations for cannabis hyperemesis syndrome (CHS), and an increased number of ED visits, in some studies up to 5-fold, due to psychiatric presentations, e.g., psychosis. Demographic factors indicate adolescents, younger adults, and disproportionately marginalized populations are more affected by RCL than others. Some differences in the impact of RCL and cannabis-related issues were noted. Conclusion: RCL is increasing the burden placed on EDs around the world, and they will need to be addressed with tailored health policies, including better state regulation, education, mental health supports, etc.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".