Medical aid in dying: A foreseen decisive role for the specialized nurse practitioner in Quebec
Bibliographic record
Abstract
The legalisation of the practice of euthanasia is gaining momentum worldwide. This paper dresses the evolution of the legalisation and development of the practice of euthanasia – medical aid in dying (MAiD) – in Canada and especially in the province of Quebec to provide understanding and guidance for health care practitioners, administrators and a larger audience. This literature review explores the phenomenon of the increasing practice of MAiD in the province of Quebec (Canada) and its possible extension into practice by specialised nurse practitioners (SNPs), it also addresses the history and issues of the practice of MAiD in this context. The analysis made it possible to define three themes that make up this phenomenon, namely a) MAiD in Canada: Implementation of the Role of NPs; b) Growing demand for MAiD in Quebec’s province; c) Issues Related to a Possible Practice of MAiD by SPNs in Quebec. Results show the rising of MAiD practised in Canada and, in Quebec, especially for an aging population and those struggling with terminal illness in order to avoid undue prolongation of suffering at the end of life. However, access to end-of-life care (EoLC) and MAiD is undermined by a shortage of doctors, bureaucratic debacles, a lack of interdisciplinary cohesion and practice and, the geographical remoteness of patients. This study also highlights the modest field of research and investigation in this specific area of practice and the need for explicit teaching about the topic of the practice of MAiD for health professionals. Finally, results show that in order to remedy this problems, the governments of Canada and Quebec and various professional orders, namely those of nurses, physicians and pharmacists, have come together to promote access to MAiD by proposing a practice project for SNPs duly trained at Master degree.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".