Cluster Analysis of Characteristics of Persons Who Died by Suicide in the Montreal Metro Transit
Bibliographic record
Abstract
BACKGROUND: Suicides occur in metro systems worldwide and patterns vary in different urban transit networks. AIMS: This study presents an in-depth analysis of 117 suicides in the Montreal Metro from 2000 to 2008 based on data obtained from coroners' investigations. METHOD: Cluster analyses were performed to identify characteristics of groups of people who kill themselves in the Montreal Metro. We also compared changes in characteristics with data from 1986 to 1995. RESULTS: We identified five clusters of suicidal persons that describe patterns of characteristics of individuals who died by suicide in the metro that may be useful for prevention. Comparisons of suicides during 2000-2008 with data from a previous study of coroners' investigations of Montreal Metro suicides during 1986-1995 indicate changes in age, isolation, types of problems, and geographic patterns. CONCLUSION: Characteristics of metro suicides may be specific to localities and can change over time. Their understanding may facilitate the development of prevention strategies tailored to these different profiles.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".