Prevalence and predictors of involuntary psychiatric hospital admissions in Ontario, Canada: a population-based linked administrative database study
Bibliographic record
Abstract
BACKGROUND: Involuntary admissions to psychiatric hospitals are common; however, research examining the trends in prevalence over time and predictors is limited. Aims To examine trends in prevalence and risk factors for involuntary admissions in Ontario, Canada. METHOD: We conducted an analysis of all mental health bed admissions from 2009 to 2013 and assessed the association between patient sociodemographics, service utilisation, pathway to care and severity characteristics for involuntary admissions using a modified Poisson regression. RESULTS: We found a high and increasing prevalence of involuntary admissions (70.7% in 2009, 77.1% in 2013, 74.1% overall). Individuals with police contact in the prior week (risk ratio (RR) = 1.20) and immigrants both experienced greater likelihood of being involuntarily admitted, regardless of control for other characteristics (RR = 1.07) (both P < 0.0001). CONCLUSIONS: We identified numerous modifiable and non-modifiable risk factors for involuntary admissions. The prevalence of involuntary admissions was high, linearly increasing over time. Declaration of interest The authors have completed the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported. This study was conducted using funding entirely from public sources. P.K. has received operational support via an Ontario Ministry of Health and Long-Term Care (MOHLTC) Health Services Research Fund Capacity Award to support this project. The Institute for Clinical Evaluative Sciences (ICES) is funded by the Ontario MOHLTC. The study results and conclusions are those of the authors, and should not be attributed to any of the funding agencies or sponsoring agencies. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. All decisions regarding study design, publication, and data analysis were made independent of the funding agencies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".