Canadian Nursing Students’ Experiences with Medical Assistance in Dying
Bibliographic record
Abstract
The recent introduction of medical assistance in dying (MAiD) has significant implications for healthcare workers, nurse educators, and society at large. Nursing students are being asked to participate directly or indirectly with a medically assisted death. Little is known about nursing students’ experiences with or attitudes toward MAiD in Canada.The purpose of this study was to explore the experiences of fourth-year nursing students in caring for patients who choose MAiD. The aim was to understand students’ perceptions of their educational preparation regarding MAiD and identify potential gaps in existing knowledge and skills. A qualitative design and thematic analysis were used. Nine senior nursing students were interviewed using semi-structured questions. Themes and corresponding sub-themes were inductively developed and include: (1) role confusion (where do student nurses fit? and fear of saying the wrong thing); (2) honouring patient autonomy (nurse as advocate-- not judge, and MAiD as ‘normal’ nursing); (3) professional tensions (entangled emotions and intellect, and surfacing hidden values); (4) students’ recommendations for education. Nursing students report support and interest in having opportunities to participate in MAiD. Nevertheless, they feel ill-equipped and perceive nurses are also unclear about their roles and responsibilities in the provision 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".