Psychotic disorder and cannabis use: Canadian hospitalization trends, 2006–2015
Bibliographic record
Abstract
INTRODUCTION: Given the recent and impending changes to the legal status of nonmedical cannabis use in Canada, understanding the effects of cannabis use on the health care system is important for evaluating the impact of policy change. The aim of this study was to examine pre-legalization trends in hospitalizations for mental and behavioural disorders due to the use of cannabis, according to demographics factors and clinical conditions. METHODS: We assessed the total number of inpatient hospitalizations for psychiatric conditions with a primary diagnosis of a mental or behavioural disorder due to cannabis use (ICD-10-CA code F12) from the Hospital Mental Health Database for ten years spanning 2006 to 2015, inclusive. We included hospitalizations from all provinces and territories except Quebec. Rates (per 100 000 persons) and relative proportions of hospitalizations by clinical condition, age group, sex and year are reported. RESULTS: Between 2006 and 2015, the rate of cannabis-related hospitalizations in Canada doubled. Of special note, however, is that hospitalizations during this time period for those with the clinical condition code "mental and behavioural disorders due to use of cannabinoids, psychotic disorder" (F12.5) tripled, accounting for almost half (48%) of all cannabis-related hospitalizations in 2015. CONCLUSION: Further research is required to investigate the reasons for the increase in hospitalizations for cannabis-related psychotic disorder. The introduction of high-potency cannabinoid products and synthetic cannabinoids into the illicit market are considered as possible factors.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".