Closer to the Truth: Admission to Multiple Psychiatric Facilities and an Inaccurate History of Hospitalization Are Strongly Associated with Inpatient Suicide
Bibliographic record
Abstract
OBJECTIVE: To investigate clinical associations within Canadian psychiatric inpatient suicides. METHOD: We conducted a case-control study comparing 98 patients who died by suicide while in hospital and 196 similarly admitted living inpatient controls. All were admitted to an Ontario psychiatric bed between 2008 and 2015 inclusive and had data collected at admission using the Resident Assessment Instrument for Mental Health (RAI-MH). The data were analyzed with bivariate tests and logistic regression modeling. RESULTS: The strongest associations with inpatient suicide were prior admission to any Ontario psychiatric bed within the previous 30 days (odds ratio [OR] = 6.13), self-harm assessed at prior admission to a psychiatric hospital other than the hospital of suicide (OR = 6.07), and prior admission to a psychiatric hospital other than the hospital of suicide in the previous year (OR = 5.38). A multivariate model using risk factors assessed at admission had an area under the curve (AUC) of 0.77. The model improved to (AUC) 0.81 using a retrospective search of all Ontario admissions to more accurately detect prior admissions. The risk model was optimized to (AUC) 0.83 when the model also included a "discrepancy" variable to denote records in which admission assessment data and retrospective search data did not agree regarding past month admissions. CONCLUSIONS: In addition to the well-known risks of suicide associated with previous suicide attempts and depressive conditions, our data suggest a particular risk of inpatient suicide associated with inpatient care in more than one hospital, particularly when the treating clinicians were unaware of recent previous admissions.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".