Long-Term Impact of the Bloor Viaduct Suicide Barrier on Suicides in Toronto: A Time-Series Analysis: Effet à long terme de la barrière anti-suicide du viaduc Bloor sur les suicides à Toronto : une analyse chronologique
Bibliographic record
Abstract
BackgroundA suicide prevention barrier was installed at Toronto's Bloor Viaduct bridge in 2003. It was associated with short-term location substitution, possibly mediated by media effects that did not persist over 1 decade. The long-term impact of the barrier is unknown.MethodsWe examined rates of suicides by jumping from the Bloor Viaduct, other bridges and by other methods using coroner's records in Toronto (1998-2020). We used interrupted time-series Poisson regression analyses to model changes in quarterly bridge-related suicides after barrier installation. A secondary analysis explored the potential substitution effects of suicide by other methods.ResultsOf 5219 suicides from 1998 to 2020, 303 were by jumping from bridges. After controlling for covariates, installation of the Bloor Viaduct suicide barrier was associated with a 49% step decrease in bridge-related suicide in the next quarter in Toronto (incidence rate ratio [IRR] = 0.51, 95% CI, 0.30 to 0.86) with no rebound increase in bridge-related suicide during the subsequent 17 years after the original drop (IRR = 0.99, 95% CI, 0.96 to 1.03). There was also no associated change in suicides by other methods after the barrier (IRR = 1.04, 95% CI, 0.90 to 1.20).ConclusionsContrary to initial findings, these results indicate an enduring suicide prevention effect of the Bloor Viaduct suicide barrier. They support the long-term utility of structural interventions at high-frequency sites for suicide.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".