The rocks and hard places of MAiD: a qualitative study of nursing practice in the context of legislated assisted death
Bibliographic record
Abstract
BACKGROUND: Medical Assistance in Dying (MAiD) was legalized in Canada in June, 2016. The Canadian government's decision to legislate assisted dying, an approach that requires a high degree of obligation, precision, and delegation, has resulted in unique challenges for health care and for nursing practice. The purpose of this study was to better understand the implications of a legislated approach to assisted death for nurses' experiences and nursing practice. METHODS: The study used a qualitative approach guided by Interpretive Description. Semi-structured interviews were conducted with 59 registered nurses and nurse practitioners. Interviews were audio-recorded, transcribed, and managed using qualitative analysis software. Analysis followed a procedure of data immersion, open coding, constant comparative analysis, and the construction of a thematic and interpretive account. RESULTS: Nurses in this study described great variability in how MAiD had been enacted in their work context and the practice supports available to guide their practice. The development of systems to support MAiD, or lack thereof, was largely driven by persons in influential leadership positions. Workplaces that supported a range of nurses' moral responses to MAiD were most effective in supporting nurses' well-being during this impactful change in practice. Participants cited the importance of teamwork in providing high quality MAiD-related care; although, many worked without the benefit of a team. Nursing work related to MAiD was highly complex, largely because of the need for patient-centered care in systems that were not always organized to support such care. In the absence of adequate practice supports, some nurses were choosing to limit their involvement in MAiD. CONCLUSIONS: Data obtained in this study suggested that some workplace contexts still lack the necessary supports for nurses to confidently meet the precision required of a legislated approach to MAiD. Without accessible palliative care, sufficient providers, a supportive team, practice supports, and a context that allowed nurses to have a range of responses to MAiD, nurses felt they were legally and morally at risk. Nurses seeking to provide the compassionate care consistent with such a momentous moment in patients' lives, without suitable supports, find themselves caught between the proverbial rock and hard place.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 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".