Étude et cartographie des hotspots suicidaires dans la région Auvergne-Rhône-Alpes
Bibliographic record
Abstract
While suicides often occur in private homes; a notable proportion take place in public locations, known as "suicide hotspots". These sites are often characterized by their frequent usage, isolation, and media attention. Preventing suicides at these hotspots is a critical component of the national suicide prevention strategy. The first two steps are identifying these hotspots and intervening specifically at each one. However, there is currently no systematically accessible tool to identify these at-risk locations. This study aims to identify the suicide hotspots in the Auvergne-Rhône-Alpes region by analyzing forensic reports of individuals who died by suicide in this region. The study also aims to examine the epidemiological characteristics of those who died by suicide, the locations and methods chosen, as well as the involvement of their social circle. Data were collected from the Institute of Legal Medicine (IML) in Grenoble, Clermont-Ferrand, and Saint-Étienne, from 2016 to 2022. It was conducted in collaboration with the Papageno program. Over the 7-year period, 26 suicide hotspots were identified, accounting for 90 suicides. Two main hotspots alone represented about a quarter of the suicides in these locations. Suicides in public places primarily involve middle-aged men with risk factors for suicidal behavior. The most common methods of suicide were voluntary submersion and hanging. Primary hotspots included rivers, dams, bridges, woods, and forests, sometimes located tens of kilometers away from the victims' homes. The identification of suicide hotspots using forensic department records demonstrates both reliability and accuracy. The significant number of recorded events raises important questions regarding the definition and prioritization of these hotspots. However, the completeness of this dataset could be enhanced through more systematic documentation of both discovery and act locations in medico-legal reports. This study underscores the crucial role of forensic pathologists in public health, contributing to data collection that serves the collective interest. Expanding this methodology to other regions prospectively would facilitate the establishment of a national, continuous monitoring system, as recommended in suicide prevention strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".