The impact of recreational cannabis legalization on cannabis-related acute care events among adults with schizophrenia
Bibliographic record
Abstract
ObjectiveCannabis use may reduce the effectiveness of antipsychotic medication and treatment adherence in schizophrenia patients. This study examined the impact of cannabis legalization on cannabis-related acute care among schizophrenia patients. MethodsUsing health administrative data in Ontario, 119,848 individuals who were diagnosed with schizophrenia prior to legalization were identified. An interrupted time-series model was used to study the effect of cannabis-related policy on hospitalizations in individuals diagnosed with schizophrenia.ResultsWe found that legalization of cannabis flower and herb sales was associated with an immediate increase in hospitalization by 233.6% (95%CI 1.59-3.414) for men and 216.1% (95%CI 1.59-3.390) for women with schizophrenia. ConclusionsOther jurisdictions seeking to legalize recreational cannabis should consider preventive strategies to reduce unintended consequences among vulnerable patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".