Trends in Involuntary Psychiatric Hospitalization in British Columbia: Descriptive Analysis of Population-Based Linked Administrative Data from 2008 to 2018
Bibliographic record
Abstract
IntroductionInvoluntary psychiatric hospitalization occurs when someone with a serious mental disorder requires treatment without their consent. Trends vary globally, and currently, there is limited data on involuntary hospitalization in Canada. We examine involuntary hospitalization trends in British Columbia, Canada, and describe the social and clinical characteristics of people ages 15 and older who were involuntarily hospitalized between 2008/2009 and 2017/2018.MethodWe used population-based linked administrative data to examine and compare trends in involuntary and voluntary hospitalizations for mental and substance use disorders. We described patient characteristics (sex/gender, age, health authority, income, urbanity/rurality, and primary diagnosis) and tracked the count of involuntarily hospitalized people over time by diagnosis. Finally, we examined population-based prevalence over time by age and sex/gender.ResultsInvoluntary hospitalizations among British Columbians ages 15 and older rose from 14,195 to 23,531 (65.7%) between 2008/2009 and 2017/2018. Apprehensions involving police increased from 3,502 to 8,009 (128.7%). Meanwhile, voluntary admissions remained relatively stable, with a minimal increase from 17,651 in 2008/2009 to 17,751 in 2017/2018 (0.5%). The most common diagnosis for involuntary patients in 2017/2018 was mood disorders (25.1%), followed by schizophrenia (22.3%), and substance use disorders (18.8%). From 2008/2009 to 2017/2018, the greatest increase was observed for substance use disorders (139%). Over time, population-based prevalence increased most rapidly among women ages 15–24 (162%) and men ages 15–34 (81%) and 85 and older (106%).ConclusionFindings highlight the need to strengthen the voluntary care system for mental health and substance use, especially for younger adults, and people who use substances. They also signal a need for closer examination of the use of involuntary treatment for substance use disorders, as well as further research exploring forces driving police involvement and its implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.129 | 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 teacher head, 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".