Suicide by different methods in Toronto: A quantitative study examining of 23-years of coronial records
Bibliographic record
Abstract
The objective of the study is to understand the characteristics of people who died by different suicide methods in Toronto, Canada. Suicide cases were identified by the Office of the Chief Coroner of Ontario (1998-2020). Demographic and clinical variables were retrieved. All suicide deaths were classified into different groups based on suicide method. Bivariate analyses and multinomial logistic regressions were performed to compare their demographic and clinical characteristics. Hanging (N = 1721), jumping from height (N = 1280), and poisoning (N = 955) were the most common suicide methods in Toronto. Those who died by hanging were more likely to be married or in common law relationships, live with others, experience employment/financial/academic-related stressors and die at home. People who died by jumping from height had a higher likelihood of having a psychiatric and/or emergency department visit in the past week and having schizophrenia or related disorders/symptoms. People who died by poisoning had higher odds of being female and leaving suicide notes. They were also more likely to have previous suicide attempts, experience depression and/or bipolar disorder and have physical conditions. Specific suicide prevention strategies should be designed and implemented to account both for commonalities and differences among people who die by different suicide methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".