Nurses’ moral experiences of assisted death: A meta-synthesis of qualitative research
Bibliographic record
Abstract
BACKGROUND:: Legislative changes are resulting in assisted death as an option for people at the end of life. Although nurses' experiences and perspectives are underrepresented within broader ethical discourses about assisted death, there is a small but significant body of literature examining nurses' experiences of caring for people who request this option. AIM:: To synthesize what has been learned about nurses' experiences of caring for patients who request assisted death and to highlight what is morally at stake for nurses who undertake this type of care. DESIGN:: Qualitative meta-synthesis. METHODS:: Six databases were searched: CINAHL, Medline, EMBASE, Joanna Briggs Institute, PsycINFO, and Web of Science. The search was completed on 22 October 2014 and updated in February 2016. Of 879 articles identified from the database searches, 16 articles were deemed relevant based on inclusion criteria. Following quality appraisal, 14 studies were retained for analysis and synthesis. RESULTS:: The moral experience of the nurse is (1) defined by a profound sense of responsibility, (2) shaped by contextual forces that nurses navigate in everyday end-of-life care practice, and (3) sustained by intra-team moral and emotional support. DISCUSSION:: The findings of this synthesis support the view that nurses are moral agents who are deeply invested in the moral integrity of end-of-life care involving assisted death. The findings further demonstrate that to fully appreciate the ethics of assisted death from a nursing standpoint, it is necessary to understand the broader constraints on nurses' moral agency that operate in everyday end-of-life care. ETHICAL CONSIDERATIONS:: Research ethics board approval was not required for this synthesis of previously published literature. CONCLUSION:: In order to understand how to enact ethical practice in the area of assisted death, the moral experiences of nurses should be investigated and foregrounded.
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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 | 0.093 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".