Medical Assistance in Dying (MAiD): Ten Things Leaders Need to Know
Bibliographic record
Abstract
The provision of MAiD will be in flux for a few years, as legislative challenges are underway. This article addresses what leaders need to know and do to support nurses today and in the future regarding care of patients choosing MAiD. Drawing on complexity leadership theory and research into nurses' experiences in caring for persons choosing MAiD, we share 10 simple yet foundational things a leader must know. Underpinning our key messages are current evidence and familiar nursing concepts such as end-of-life care, death trajectories, conscientious objection, scope of practice, ethics, sense-making and care cultures. These key messages are embedded in a framework of leadership practices where attention to inter-relationships, emergence and innovation are highlighted. They provide nurse leaders with concrete actions to inspire a team dynamic for creating inclusive cultures of quality care. Leadership is needed across healthcare settings where MAiD is being enacted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".