La judiciarisation des patients psychiatriques : éléments de réflexion et applications pratiques
Bibliographic record
Abstract
Parmi les solutions possibles en réponse aux gestes violents commis par des patients psychiatriques, la judiciarisation demeure une alternative souvent méconnue mais qui peut pourtant s’avérer bénéfique et efficace. Comment choisir, de façon éclairée, la judiciarisation face à la violence de certains patients ? Lorsque cette voie est choisie, comment s’applique-t-elle et quelles sont les démarches à suivre ? Pour répondre à ces questions, il nous est apparu essentiel d’engager d’abord une réflexion plus globale sur l’utilisation de la sanction en psychiatrie. Nous évoquerons ensuite les avantages et inconvénients du processus de judiciarisation et, finalement nous illustrerons les modalités d’application de la judiciarisation et telles qu’elles ont été conçues et réfléchies à la clinique de dangerosité de l’Institut Philippe Pinel de Montréal.
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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 | 0.042 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".