Medical assistance in dying (MAiD): Canadian nurses’ experiences
Bibliographic record
Abstract
Medical assistance in dying (MAiD) represents a historic change in Canadian society and the provision of end-of-life care. In this descriptive narrative inquiry, 17 nurses were interviewed during the first 6 months of assisted dying becoming a legal option for patients in Canada. Nurses' experiences of either providing care for a patient who had chosen MAiD, or declining to participate in MAiD, were explored. Findings describe three themes and eight storylines of the impact of MAiD on nurses' view of the profession, clinical practice, and personally. While most nurses perceived MAiD as an extension of the profession and their nursing practice, a small number also expressed moral distress as they grappled with assisted dying. Narratives illustrated an ongoing sensemaking process and spectrum of emotions. These findings offer insight and provide direction for nurses and managers in this new clinical and legal reality. Further research is needed to understand more fully the moral distress of some nurses, as well as the importance of communicating openly and nonjudgmentally with patients, families, and the health-care team.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".