Chapitre 7. Les enjeux de l’aide médicale à mourir en contexte de sclérose latérale amyotrophique : une revue de la littérature
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease that leads some people with the disease to consider medical assistance in dying (MAiD). In this article, we describe how a variety of moral problems can emerge from this particular context and affect the well-being of people with ALS, their loved ones, and their caregivers. As MAiD is framed by specific eligibility criteria, broadening its eligibility is often proposed to address these issues. This critical review of the literature aims to identify moral issues relating to ALS that may persist or arise in the event of such widening. The MEDLINE, EMBASE CINAHL and Web of Science databases were searched using 4 search combinations to capture insights from existing literature on ethics, MAiD and ALS (N=41). A thematic content analysis highlighted 3 contextual categories where moral issues emerge (the experience of the disease, the choice of how to die, and the implementation of MAiD). Two important observations are discussed: 1) there are differences in perspective between stakeholders, which can lead to disagreement, but some similarities of perspective also exist; 2) the widening of MAiD eligibility mainly concerns moral issues related to the choice of how to die, and thus constitutes a partial solution to the problems identified.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".