The Impact of Recreational Cannabis Legalization and COVID-19 on Injury-related Emergency Department Visits and Hospitalizations in Canada
Bibliographic record
Abstract
Background: The effects of recreational cannabis legalization (RCL) in Canada and the COVID-19 pandemic on the rates of injuries in the Canadian population are largely unknown.Methods: An interrupted time series analysis using population-based data from 2010 to 2021 was used to assess the population-level effect of RCL and COVID-19 on rates of emergency department (ED) visits and hospitalizations for injury. The Mantel-Haenszel method was used to estimate weighted odds ratios for injury-related ED visits after RCL only. Results: There were no notable changes in rates of ED visits or hospitalizations for injury after RCL; however, after lockdowns due to COVID-19 began there was an immediate decrease in ED visits for most injury types followed by gradual increases over time. Conclusions: These results have implications for Canadian and international public health policies, health planning, and injury prevention and mitigation measures.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".