Medically Assisted Dying in Canada: “Beautiful Death” Is Transforming Nurses’ Experiences of Suffering
Bibliographic record
Abstract
BACKGROUND: Nurses witness pain and distress up close and consequently experience their own suffering. A narrative study of Canadian nurses' participating in medical assistance in dying found nurses' previous witnessing of unresolved end-of-life suffering has shaped their acceptance of medical assistance in dying. Little is known about the impact of participating in medically assisted dying on nurses' suffering. PURPOSE: To explore how nurses' overall experience of suffering is shaped by participating in medical assistance in dying. METHODS: Qualitative secondary analysis using narrative inquiry and thematic analysis. RESULTS: Nurses' narratives are chronologically organized addressing experiences of suffering before medical assistance in dying was a legal option and after its implementation. An overarching narrative before the availability of medical assistance in dying is (1) a culture of nurses' taken-for-granted suffering: feeling terrible. After medical assistance in dying, two key narratives describe (2) transformational feelings of a beautiful death and (3) residual discomfort. Nurses found their suffering transformed when participating in medical assistance in dying; end-of-life care was satisfying and gratifying. And yet, unanswered questions due to worries of becoming desensitized and ongoing deeper questioning remain. CONCLUSIONS: Participating in medical assistance in dying has positively impacted nurses and starkly contrasts their previous experiences caring for those with unbearable suffering. Further research is needed to explore becoming desensitized and long-term emotional impact for nurses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".