Nursing students' attitudes toward, and willingness to participate in Medical Assistance in Dying (MAiD) in the Canadian context : survey development
Bibliographic record
Abstract
Background: In 2016 Canada passed legislation which legalised Medical Assistance in Dying (MAiD), allowing eligible Canadians the ability to receive a lethal substance to end their life under certain circumstances. Nurses in Canada have a significant role in providing care to clients before, during, and after a MAiD death. Nurses’ and nursing students’ experiences with MAiD thus far have been complex. The purpose of this study was to develop a survey to assess nursing students’ attitudes toward and willingness to participate in MAiD in the unique Canadian context. Methods: This study created and initially validated a survey that can be used at a future date to assess nursing students’ and their attitudes toward, and willingness to participate in, MAiD. This study utilized item generation techniques, a Delphi method with panel of expert nursing faculty, and a cognitive interview focus group with nursing students to prioritize, refine, and validate the survey questions. Results: The final survey consisted of 45 questions including four case studies. Categories of the survey included questions relating to: participant demographics, experiences with end-of-life and MAiD, knowledge of MAiD, agreement/disagreement with MAiD, influences of beliefs about MAiD, willingness to participate in roles related to MAiD, and clinical case scenarios. Discussion: This study provided a significant step in being able to assess nursing students’ attitudes toward MAiD in Canada, and the results aligned with existing literature. Each category of the survey proved to be an important area of future study, with several controversies providing a focus for future research.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".