Suicide Deaths by Gas Inhalation in Toronto, Canada – An Observational Study of Emerging Methods of Suicide From 1998 to 2020
Bibliographic record
Abstract
Abstract: Background: Inert gases are an emerging means of suicide in Toronto, Canada. Trends in suicide by these methods change over time, yet long-term patterns remain uncharacterized in cities like Toronto. Aims: To update trends in suicide using inhalational gas and explore the profiles of individuals using different methods in Toronto. Methods: Suicide deaths were identified from coroner’s records and classified by suicide methods. Time trends were explored, and bivariate analyses were performed to characterize differences in profiles between groups. Results: There were 229 suicide deaths by inert gas between 1998 and 2020. For 2016–2020, suicide by nitrogen increased by 100%, whereas there was a decrease in suicide by helium (−38%) and charcoal burning (−57%) compared to 2011–2015. Males comprised a higher proportion of inhalational gas deaths compared to other methods. Individuals who died by compressed gas and charcoal burning were more likely to have left suicide notes compared to people who died by other methods. Limitations: The number of suicide deaths by gas inhalation may be underestimated due to potential misclassification. Conclusions: Suicide prevention strategies including restricting access to suicidal means, providing helpline information on the products, and responsible media reporting should each be advocated for.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".