Constructing Good Nursing Practice for Medical Assistance in Dying in Canada: An Interpretive Descriptive Study
Bibliographic record
Abstract
Nurses play a central role in Medical Assistance in Dying (MAiD) in Canada. However, we know little about nurses' experiences with this new end-of-life option. The purpose of this study was to explore how nurses construct good nursing practice in the context of MAiD. This was a qualitative interview study using Interpretive Description. Fifty-nine nurses participated in semi-structured telephone interviews. Data were analyzed inductively. The findings illustrated the ways in which nurses constructed artful practice to humanize what was otherwise a medicalized event. Registered nurses and nurse practitioners described creating a person-centered MAiD process that included establishing relationship, planning meticulously, orchestrating the MAiD death, and supporting the family. Nurses in this study illustrated how a nursing gaze focused on relationality crosses the moral divides that characterize MAiD. These findings provide an in-depth look at what constitutes good nursing practice in MAiD that can support the development of best practices.
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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 | 0.019 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".