How High? Trends in Cannabis Use Prior to First Admission to Inpatient Psychiatry in Ontario, Canada, between 2007 and 2017
Bibliographic record
Abstract
OBJECTIVES: To examine the trends in cannabis use within 30 days of first admission to inpatient psychiatry in Ontario, Canada, between 2007 and 2017, and the characteristics of persons reporting cannabis use. METHODS: = 81,809). RESULTS: Across all years, 20.1% of patients reported cannabis use within 30 days of first admission. Use increased from 16.7% in 2007 to 25.9% in 2017, and the proportion with cannabis use disorders increased from 3.8% to 6.0%. In 2017, 47.9% of patients aged 18 to 24 and 39.2% aged 25 to 34 used cannabis, representing absolute increases of 8.3% and 10.7%, respectively. Increases in cannabis use were found across almost all diagnostic groups, with the largest increases among patients with personality disorders (15% increase), schizophrenia or other psychotic disorders (14% increase), and substance use disorders (14% increase). A number of demographic and clinical factors were significantly associated with cannabis use, including interactions between schizophrenia and gender (area under the curve = 0.88). CONCLUSIONS: As medical cannabis policies in Canada have evolved, cannabis use reported prior to first admission to inpatient psychiatry has increased. The findings of this study establish a baseline for evaluating the impact of changes in cannabis-related policies in Ontario on cannabis use prior to admission to inpatient psychiatry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".