Incidence and Predictors of Suicide Attempts and Suicide Deaths Among Individuals Recently Hospitalized for a Mental Disorder
Bibliographic record
Abstract
Objective: To examine the incidence and predictors of suicide attempts and deaths in the year after psychiatric hospitalization. Methods: A population-based dataset was used to develop a cohort of individuals 18 years or older admitted with a mental disorder (defined by ICD-10 codes) from 2005 to 2016 (n = 26,975) in Manitoba, Canada. Using Cox regression, hazard ratios were calculated for each covariate among those who attempted and died by suicide in the year following hospitalization, while adjusting for confounders. Results: In the year following hospitalization for a mental disorder, 0.7% of the individuals died by suicide and 3.5% attempted suicide. Statistically significant risk factors for suicide in the year after discharge from psychiatric hospitalization included male sex (hazard ratio , 1.47; 95% confidence interval , 1.10–1.97) and urban location (HR, 1.37; 95% CI, 1.02–1.85) and for attempting suicide included female sex (HR, 0.63; 95% CI, 0.55–0.72), living rurally (HR, 0.66; 95% CI, 0.58–0.75), a previous mental disorder (HR, 1.63; 95% CI, 1.38–1.92), justice involvement (HR, 1.48; 95% CI, 1.28–1.70), and being on income assistance (HR, 1.17; 95% CI, 1.01–1.35) (P < .05 for all). Age (HR, 0.99; 95% CI, 0.99–0.99) (P < .05) was associated with a reduced rate of suicide attempts. Conclusions: Further research into interventions to address the identified risk factors for suicide in the recently discharged population is critical to improve management.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".