Observation des comportements agressifs des patients hospitalisés : entre devoir d’identification précoce de l’agressivité, risques de stigmatisation et exigences thérapeutiques
Bibliographic record
Abstract
L’absence d’une définition claire de la dangerosité et de la violence en psychiatrie, ainsi que le constat d’échec général des outils de prédiction ne doivent pas interrompre le développement du management des risques dans l’institution. Nous proposons l’utilisation d’une échelle d’observation des comportements agressifs qui s’inspire largement du modèle de l’OAS (Overt Agression Scale ) développée par Yudofsky et ses collaborateurs. Il s’agit d’améliorer la qualité de l’observation infirmière et, ce faisant, d’utiliser l’OAS comme un médium pluridisciplinaire d’une part, et comme outil de communication avec le patient concerné, d’autre part. Entre banalisation et stigmatisation, nous restons attentifs au besoin de sécurité du personnel soignant et aux risques d’exclusion des patients « dérangeants ».
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".