The Perceptions and Experiences of Nursing Students on Medical Assistance in Dying (MAiD)
Bibliographic record
Abstract
Background: The recent legalization, in Canada, of medical assistance in dying (MAiD) places additional responsibility on undergraduate nursing educators to prepare their students for such encounters. Undergraduate students lack familiarity with death and dying, most of which takes place outside the home in health care institutions. How well these future providers identify and communicate with and care for these patients depends on their educational preparation. Educators, along with nursing regulatory bodies, require more research to prepare curriculum and policies, respectively. Purpose: This study aimed to gain a better understanding of the experiences and perceptions of nursing students on MAiD, their current understanding of MAiD, ways their current nursing program has helped them prepare to care for a patient requesting MAiD, and areas of improvement for MAiD education within their nursing curriculum. Methods: Qualitative description methodology included Braun and Clarke’s approach to thematic analysis. Five undergraduate bachelor of nursing students in their fourth year participated in one in-person focus group session. Results: Six themes were identified: (1) inconsistency in education; (2) student knowledge of MAiD; (3) moral complexity of MAiD experienced by nursing students; (4) uncertainty; (5) strategies for integrating MAiD education; and (6) supporting patient and family autonomy. Conclusion: The results of this study highlight the importance of MAiD education in nursing and provide a snapshot of nursing students’ current understandings and perceptions of MAiD. This study suggests further studies are needed to fully analyze perceptions and experiences of nursing students, which is key for development of curricula and programs to support and facilitate the skills required of nursing students when caring for patients who request MAiD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".