Can CCTV identify people in public transit stations who are at risk of attempting suicide? An analysis of CCTV video recordings of attempters and a comparative investigation
Bibliographic record
Abstract
BACKGROUND: Suicides incur in all public transit systems which do not completely impede access to tracks. We conducted two studies to determine if we can reliably identify in stations people at risk of suicide in order to intervene in a timely manner. The first study analysed all CCTV recordings of suicide attempters in Montreal underground stations over 2 years to identify behaviours indicating suicide risk. The second study verified the potential of using those behaviours to discriminate attempters from other passengers in real time. METHODS: First study: Trained observers watched CCTV video recordings of 60 attempters, with 2-3 independent observers coding seven easily observable behaviours and five behaviours requiring interpretation (e.g. "strange behaviours," "anxious behaviour"). Second study: We randomly mixed 63 five-minute CCTV recordings before an attempt with 56 recordings from the same cameras at the same time of day, and day of week, but when no suicide attempt was to occur. Thirty-three undergraduate students after only 10 min of instructions watched the recordings and indicated if they observed each of 13 behaviours identified in the First Study. RESULTS: First study: Fifty (83%) of attempters had easily observable behaviours potentially indicative of an impending attempt, and 37 (61%) had two or more of these behaviours. Forty-five (75%) had at least one behaviours requiring interpretation. Twenty-two witnesses attempted to intervene to stop the attempt, and 75% of attempters had behaviours indicating possible ambivalence (e.g. waiting for several trains to pass; trying to get out of the path of the train). Second study: Two behaviours, leaving an object on the platform and pacing back and forth from the yellow line (just before the edge of the platform), could identify 24% of attempters with no false positives. The other target behaviours were also present in non-attempters. However, having two or more of these behaviours indicated a likelihood of being at risk of attempting suicide. CONCLUSIONS: We conclude that real time observations of CCTV monitors, automated computer monitoring of CCTV signals, and/or training of drivers and transit personnel on behavioural indications of suicide risk, may identify attempters with few false positives, and potentially save lives.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".