The Evolving Context of MAID-Related Communications for Registered Nurses in Canada
Bibliographic record
Abstract
BACKGROUND: Since legalization of Medical Assistance in Dying in Canada in 2016, nurses are increasingly faced with new and evolving communication challenges as patients in a diversity of settings and contexts contemplate their end-of-life options. PURPOSE: The purposes of this study were: 1) to develop an understanding of the nuances and challenges associated with MAID-related communication from the perspective of registered nurses, and 2) to draw on the insights arising from this analysis to reflect on the evolution of MAID communication for nurses over time. METHODS: This study represented a secondary analysis of two primary qualitative data sets, including: 74 interviews of Canadian registered nurses self-identifying as having some exposure to MAID in their clinical practice; and 47 narrative reflections volunteered by respondents to questions posed in an online MAID reflective guide for nurses. RESULTS: Nurses described evolving complexities associated with introducing and engaging with the topic of MAID with their patients, helping patients navigate access to MAID assessment, managing family and community dynamics associated with opinions and beliefs surrounding MAID, supporting patients in their planning toward a MAID death, and being there for patients and their families in the moment of MAID. CONCLUSIONS: MAID communication is highly complex, individualized, and context-specific. It is apparent that many nurses have developed an impressive degree of comfort and skill around navigating its nuances within a rapidly evolving legislative context. It is also apparent that dedicated basic and continuing MAID communication education will warranted for registered nurses in all health care settings.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".