But it’s legal, isn’t it? Law and ethics in nursing practice related to medical assistance in dying
Bibliographic record
Abstract
In June 2015, the Supreme Court of Canada struck down the Criminal Code's prohibition on assisted death. Just over a year later, the federal government crafted legislation to entrench medical assistance in dying (MAiD), the term used in Canada in place of physician-assisted death. Notably, Canada became the first country to allow nurse practitioners to act as assessors and providers, a result of a strong lobby by the Canadian Nurses Association. However, a legislated approach to assisted death has proven challenging in a number of areas. Although it facilitates a degree of accountability, precision and accessibility, it has also resulted in particular challenges negotiating the diverse perspectives of such a morally contentious act. One of these challenges is the tendency to conflate what is legal and what is moral in a modern liberal constitutionalism that places supreme value on autonomy and choice. Such a conflation tends to render invisible the legal and moral/ethical considerations necessary for nurses and nurse practitioners to remain ethical actors. In this paper, we introduce this conflation and then discuss the process of lawmaking in Canada, including the legalization of MAiD and the contributions of nursing to that legalization. We then engage in a hypothetical dialogue about the legal and moral/ethical implications of MAiD for nursing in Canada. We conclude with an appeal for morally sustainable workspaces that, when implementing MAiD, appropriately balance patient choices and nurses' moral well-being.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.102 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".