An Observational Study of Suicide Death in Homeless and Precariously Housed People in Toronto
Bibliographic record
Abstract
OBJECTIVE: Homelessness has been identified as an important risk factor for suicide death, but there is limited research characterising homeless people who die by suicide. The goal of this study is to identify personal, clinical, and suicide method-related factors that distinguish homeless and precariously housed people who die from suicide from those who are not homeless at the time of suicide. METHODS: Coroner records were reviewed for all suicide deaths in Toronto from 1998 to 2012. Data abstracted included housing status as well as other demographics, clinical variables such as the presence of mental illness, and suicide method. RESULTS: Of 3319 suicide deaths, 60 (1.8%) were homeless and 230 (6.9%) were precariously housed. Homeless and precariously housed people were each younger than nonhomeless people ( P < 0.0001). Compared with nonhomeless, homeless people were more likely to be male and less likely to be married, to have interpersonal conflict, or to leave a suicide note. Homeless people and precariously housed were more likely to have died by fall/jump than nonhomeless people (62%, 57%, and 29%, respectively). CONCLUSIONS: Homeless and precariously housed people are overrepresented among suicide deaths in a large urban center and differ demographically, clinically, and in their suicide method from nonhomeless people who die by suicide. Targeted suicide prevention strategies should aim to address factors specific to homeless people.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".