Effect of a barrier at Bloor Street Viaduct on suicide rates in Toronto: natural experiment
Bibliographic record
Abstract
OBJECTIVE: To determine whether rates of suicide changed in Toronto after a barrier was erected at Bloor Street Viaduct, the bridge with the world's second highest annual rate of suicide by jumping after Golden Gate Bridge in San Francisco. DESIGN: Natural experiment. SETTING: City of Toronto and province of Ontario, Canada; records at the chief coroner's office of Ontario 1993-2001 (nine years before the barrier) and July 2003-June 2007 (four years after the barrier). PARTICIPANTS: 14 789 people who completed suicide in the city of Toronto and in Ontario. MAIN OUTCOME MEASURE: Changes in yearly rates of suicide by jumping at Bloor Street Viaduct, other bridges, and buildings, and by other means. RESULTS: Yearly rates of suicide by jumping in Toronto remained unchanged between the periods before and after the construction of a barrier at Bloor Street Viaduct (56.4 v 56.6, P=0.95). A mean of 9.3 suicides occurred annually at Bloor Street Viaduct before the barrier and none after the barrier (P<0.01). Yearly rates of suicide by jumping from other bridges and buildings were higher in the period after the barrier although only significant for other bridges (other bridges: 8.7 v 14.2, P=0.01; buildings: 38.5 v 42.7, P=0.32). CONCLUSIONS: Although the barrier prevented suicides at Bloor Street Viaduct, the rate of suicide by jumping in Toronto remained unchanged. This lack of change might have been due to a reciprocal increase in suicides from other bridges and buildings. This finding suggests that Bloor Street Viaduct may not have been a uniquely attractive location for suicide and that barriers on bridges may not alter absolute rates of suicide by jumping when comparable bridges are nearby.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".