Supporting nursing roles in medical assistance in dying: Development and evaluation of an evidence-based reflective guide
Bibliographic record
Abstract
Objective: To develop and evaluate an evidence-based online Reflective Guide to prepare Registered Nurses and Nurse Practitioners for important professional, personal, and relational roles in MAID in Canada. Methods: The Reflective Guide was developed inductively from qualitative interviews with 120 Canadian nurses. The online Guide contains a 15 min documentary video and five areas of content: nurses' experiences, making moral sense of MAID, best practices, common dilemmas, and self-care strategies. Online visitors to the Guide were asked to participate in a mixed-method evaluation of the Guide. Results: Participants rated their experiences with the Guide highly, indicating that it helped them develop further insights about MAID and strengthened their practice. Qualitative responses revealed an array of emotions that resulted from the philosophic, moral, and professional wrestling that is characteristic of this new practice. Conclusion: The positive responses to the Guide, and the complexity of the responses submitted by respondents, attest to the effectiveness of the Guide and the importance of preparing nurses for the personal and professional aspects of MAID-related practice. Innovation: The MAID Reflective Guide is an effective innovation for nurses as evidenced by its uptake. In the first year the Guide received 2300 unique learners from 30 countries.
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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 | 0.102 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".