L'accès et la pratique de l'euthanasie et du suicide assisté pour motifs psychiatriques
Bibliographic record
Abstract
Euthanasia or end-of-life medical assistance has been debated for decades in medicine, especially in oncology or intensive care. Seven countries are in favor of euthanasia and/or assisted suicide: Switzerland, the Netherlands, Belgium, Luxembourg, Canada, Colombia, and six US states. This practice is far from being a simple concern. This asks medical, ethical, cultural and legal issues at the same time. On the one hand, it makes use of medical notions, like unbearable and unappeasable suffering and prognosis, or even ethics notions. On the other hand, it is governed by a legal framework which is more or less well defined depending on the countries. The frequency and practices are quite different from one country to another. These deaths accounted for up to 4,4% of deaths in 2017 (for the Netherlands), and mostly involved elderly patients with cancer. The integration of mental suffering into the law legalizing its access in some countries has made the issue even more complex. Switzerland, the Netherlands, Belgium and Luxembourg, four countries in Europe, are the countries that accept euthanasia and/or assisted suicide for psychiatric reasons, already for decades in the case of Switzerland and the Netherlands. This practice is controversial and opens debates within the medical profession, in countries concerned and beyond, internationally. Concerns about intricacy of mood symptoms, reasoning bias and cognitive disruptions impairing judgment, access and consent to care, and the assessment of mental suffering are particularly debated.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".