Policy analysis and evaluation of effectiveness of a suicide prevention initiative in Montreal
Bibliographic record
Abstract
Background Jacques-Cartier Bridge in Montreal, Canada, was one of the sites with the highest number of suicides in North America. Following extensive research and lobbying by an expert group, a suicide prevention barrier was constructed on the bridge. Aims/Objectives/Purpose (1) To analyse factors contributing to the successful implementation of this suicide prevention initiative; (2) To investigate if the barrier led to displacement of suicides to other jumping sites. Methods We used Shiffman and Smith's Political Priority Framework to analyse the characteristics, ideas, actor strength, and political context associated with the initiative. Poisson regression was used to assess changes in annual rates of suicide by jumping from Jacques-Cartier Bridge, other bridges and other sites after installation of the suicide barrier. Results/Outcomes The presence of powerful, credible and committed actors and coherence of ideas used to convey messages about suicide and its prevention, based on scientific evidence, were important for the success of the prevention initiative. Support of the media was also critical. Results show no evidence of displacement to other jumping sites after installation of the barrier. Significance/Contribution to Field The study provides useful information on the successful implementation of a public health initiative at a local level that can inform future policy initiatives, not only for suicide prevention but other health outcomes. The barrier on Jacques-Cartier Bridge was effective in reducing suicides. The design of a barrier is important for its effectiveness and is preferably considered in the construction of new bridges with the potential to become symbolic suicide sites.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".