Suicide following discharge from inpatient psychiatric care: A retrospective case control study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The period following discharge from psychiatric hospitalization is associated with high suicide risk. This study sought to determine the rate and associated risk factors of individuals who die by suicide after discharge in Ontario, Canada. METHODS: This retrospective case-control study, spanning from 2006-2018, utilized Ontario data to compare individuals who died by suicide within 7, 30, and 90 days of discharge with controls. Clinical, demographic, and healthcare utilization factors were compared. A Cox proportional hazards model was utilized to determine factors independently associated with suicide. RESULTS: Across 615,067 psychiatric discharges, there were 320, 771, and 1325 suicide deaths within a 7-, 30-, and 90-day period respectively. These deaths correspond to a suicide rate of 2713, 1525, and 882 deaths per 100,000 person-years and 0.52, 1.25, and 2.15 suicides per 1000 discharged individuals. Cases were more likely to be male, aged 45-54, involve unplanned discharge and a history of suicidal behaviour, and admitted for mood or adjustment disorders. Rural residence, income, medical comorbidity, alcohol, substance use disorder, and psychotic illness were not significantly associated with suicide. Healthcare service utilization did not differ significantly. CONCLUSIONS: The suicide rate is highest immediately following discharge and remains elevated above that of the general Canadian population throughout the 90 days afterward. Risk factors identified include mood disorders, male sex, middle age, shorter length of stay, and unplanned discharge - consistent with previous work. Individuals with unplanned discharges and shorter lengths of stay may be good candidates for closer follow-up to mitigate risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".