Bibliographic record
Abstract
Chaque année, plus de 800 000 personnes meurent par suicide dans le monde. Le développement d’outils de prévention apparaît comme un enjeu majeur de santé publique. Les processus d’intelligence artificielle (IA) pourraient fournir un support efficient à l’aide à la décision en prévention du suicide grâce à l’accès aux données cliniques et à l’interprétation des signaux de risque de passage à l’acte.La législation en vigueur apporte un cadre général sur la protection des droits fondamentaux, celle des patients, ou la sécurité des produits. Toutefois, certaines spécificités de l’IA en compliquent l’application et le contrôle. Or, lorsque des innovations de ruptures revendiquent une partie de la décision médicale, elles convoquent une réflexion sur le plan du droit.Dans un cadre interdisciplinaire, cet article envisage les enjeux juridiques qui accompagnent le déploiement de processus d’IA dans la prévention des risques suicidaires et formule quelques éléments de réponses.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".