Bibliographic record
Abstract
OBJECTIVE: Suicide is an important cause of premature mortality in people suffering from schizophrenia. This study aimed to identify demographic, personal, and suicide-specific features that distinguish suicide in people with schizophrenia from those with another severe mental illness (bipolar disorder) and those with neither illness. METHOD: We conducted a coroner's chart review for 2,886 suicide victims in Toronto from 1998 to 2010. Diagnoses were made based on coroner interviews with available informants including family members, acquaintances, the deceased's physician(s) and/or review of medical records. Of the total, 258 formed what we defined as the schizophrenia group (204 schizophrenia, 34 unspecified psychotic illness, and 20 schizoaffective disorder). Of the remainder, 169 had bipolar disorder, and 2,459 had neither illness. One-way analysis of variance (ANOVA) tests and χ² tests were conducted to examine differences between the groups. RESULTS: The group with schizophrenia was the youngest (mean age for schizophrenia, 41.0 years; bipolar disorder, 43.3 years; and neither, 47.7 years; P < .001), most likely to have never been married (schizophrenia, 75.6%; bipolar disorder, 57.4%; and neither, 52.9%; P < .001), most likely to be living in temporary/assisted housing or jail (schizophrenia, 9.3%; bipolar disorder, 5.4%; and neither, 3.2%; P < .001), and least likely to have experienced a recent stressor (schizophrenia, 26.7%; bipolar disorder, 37.9%; and neither, 54.1%; P < .001). The schizophrenia group was the most likely to use a violent cause of death, specifically by fall from a height or by jumping in front of a vehicle (schizophrenia, 81.4%; bipolar disorder, 58.0%; and neither, 73.1%; P < .001). CONCLUSIONS: There are important demographic and suicide-related differences between suicide victims with and without schizophrenia. Notably, suicide in schizophrenia overall appears to be more illness driven and occurs by more violent means than in the bipolar disorder group or those with neither illness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".