How to prevent suicide events at the community level
Bibliographic record
Abstract
How to prevent suicide events at the community level. To be delivered as a panel presentation and discussion during the 10th World Conference on Injury Prevention and Safety Promotion, London, UK. Chaired by Professor Leif Svanstrom, WHO CC on Comm Safety Promotion. Background The Safe Community movement has spent 35 years developing local prevention of accidents, 15–20 years to prevent injuries caused by violence and a decade or so trying to meet the expectations of communities facing a growing problem of preventing suicidal episodes. Panel participants Dr Choung Ah Lee, MD, S Korea: The role of hospital and emergency departments in suicide prevention; Professor Lars-Gunnar Horte, Sweden: How common are suicidal events? Epidemiological issues; Professor Jan Beskow, Sweden: Accidents and Suicide attempts- two sides of the same coin? Professor Leif Svanstrom, Karolinska Institutet, Sweden: The role of the International Safe Community Movement in preventing suicidal events.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".