Effectiveness of partial restriction of access to means in jumping suicide: lessons from four bridges in three countries
Bibliographic record
Abstract
Abstract Aims Restricting access to means by installing physical barriers has been shown to be the most effective intervention in preventing jumping suicides on bridges. However, little is known about the effectiveness of partial restriction with interventions that still allow jumping from the bridge. Methods This study used a quasi-experimental design. Public sites that met our inclusion criteria were identified using Google search and data on jumping suicides on Bridge A (South Korea), Bridges B and C (the United States) and Bridge D (Canada) were obtained from the relevant datasets. Incidence rate ratios (IRRs) were estimated using Poisson regressions comparing suicide numbers before and after the installation of physical structures at each site. Results Fences with sensor wires and spinning handrails installed above existing railings on the Bridge A, and fences at each side of the entrances and the midpoint of main suspension cables on the Bridge D were associated with significant reductions in suicides (IRR 0.37, 95% Confidence Interval (CI) 0.26 − 0.54; 0.26, 95% CI 0.09 − 0.76). Installation of bird spike on the parapet on the Bridge B, and fences at the front of seating alcoves on the Bridge C were not associated with changes in suicides (1.21, 95% CI 0.88 − 1.68; 1.49, 95% CI 0.56 − 3.98). Conclusions Partial means restriction (such as fences with sensor wires and spinning bars at the top, and partial fencing at selected points) on bridges appears to be helpful in preventing suicide. Although these interventions are unlikely to be as effective as interventions that fully secure the bridge and completely prevent jumping, they might best be thought of as temporary solutions before more complete or permanent structures are implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".