Medical Assistance in Dying: A Review of Canadian Nursing Regulatory Documents
Bibliographic record
Abstract
Canada's legalization of Medical Assistance in Dying (MAiD) in 2016 has had important implications for nursing regulators. Evidence indicates that registered nurses perform key roles in ensuring high-quality care for patients receiving MAiD. Further, Canada is the first country to recognize nurse practitioners as MAiD assessors and providers. The purpose of this article is to analyze the documents created by Canadian nursing regulatory bodies to support registered nurse and nurse practitioner practice in the political context of MAiD. A search of Canadian provincial and territorial websites retrieved 17 documents that provided regulatory guidance for registered nurses and nurse practitioners related to MAiD. Responsibilities of registered nurses varied across all documents reviewed but included assisting in assessment of patient competency, providing information about MAiD to patients and families, coordinating the MAiD process, preparing equipment and intravenous access for medication delivery, coordinating and informing health care personnel related to the MAiD procedure, documenting nursing care provided, supporting patients and significant others, and providing post death care. Responsibilities of nurse practitioners were identified in relation to existing legislation. Safety concerns cited in these documents related to ensuring that nurses understood their boundaries in relation to counseling versus informing, administering versus aiding, ensuring safeguards were met, obtaining informed consent, and documenting. Guidance related to conscientious objection figured prominently across documents. These findings have important implications for system level support for the nursing role in MAiD including ongoing education and support for nurses' moral decision making.
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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 | 0.031 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.047 | 0.066 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".