Psychiatrie médico-légale. Envergure, responsabilités éthiques et conflits de valeurs
Bibliographic record
Abstract
To write about ethics in specialties that straddle the lines of multiple systems cannot be done without discussing values and decisional rules that underlie each one of those systems. By virtue of its multiple associations, forensic psychiatry is an archetype of such specialties ; it works within a set of values that might be viewed as antithetical, even irreconcilable, with other aspects of psychiatry. The extensive scope of action of forensic psychiatry compels its practitioners to hold alternate world views and to apply decisional rules that may clash with the classical values and ethical considerations of medicine (Weisstub, 1980). In this article, following an historical précis, the authors review the scope of action of forensic psychiatry as the basis for the definition of this subspecialty. The concepts, themes and controversies pertaining to the ethical practice of this specialty will be reflected upon in the light of issues encountered in actual practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".