De l’aide médicale à mourir au Québec : une analyse du contexte et de la pratique infirmière en soins palliatifs
Bibliographic record
Abstract
Context : Quebec's "medical aid in dying" (MAID) is a medical intervention mostly practiced in palliative care units. MAID results from a deep revolution within Quebec's ethics regarding end-of-life care. However, there is a lack of empirical studies regarding nursing practice within the context of MAID.Aim : To conduct a literature review of legal and scientific data, as well as data from the media, related to the patient experience of nursing practices in palliative care for patients in Quebec who have requested MAID.Methodology : Burn, Grove, and Sutherland's methodology allowed us to extract and analyze six empirical studies from the CINAHL database. We also analyzed 17 media articles and one legal paper that documents the legalization of MAID in Quebec.Results : There has been a strict law governing MAID since 2015, but some major social events and issues have recently allowed for the expansion of the practice. The literature review allowed us to identify two themes : 1). the nature of nursing practice within MAID, and 2). the need for nursing education that addresses the complex aspects of MAID.Discussion and conclusion : We discuss the effects of the domination of the biomedical narrative on MAID and the collapse of Quebec's health system, which both negatively impact nursing practice in the context of MAID.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".