Clinician responses to legal requests for hastened death: a systematic review and meta-synthesis of qualitative research
Bibliographic record
Abstract
BACKGROUND: The legalisation of medical assistance in dying in numerous countries over the last 20 years represents a significant shift in practice and scope for many clinicians who have had little-to-no training to prepare them to sensitively respond to patient requests for hastened death. AIMS: Our objective was to review the existing qualitative literature on the experiences of healthcare providers responding to requests for hastened death with the aim of answering the question: how do clinicians make sense of, and respond to patients' expressed wishes for hastened death? METHODS: We performed a systematic review and meta-synthesis of primary qualitative research articles that described the experiences and perspectives of healthcare professionals who have responded to requests for hastened death in jurisdictions where MAiD (Medical Assistance in Dying) was legal or depenalised. A staged coding process was used to identify and analyse core themes. RESULTS: Although the response to requests for hastened death varied case-by-case, clinicians formulated their responses by considering seven distinct domains. These include: policies, professional identity, commitment to patient autonomy, personal values and beliefs, the patient-clinician relationship, the request for hastened death and the clinician's emotional and psychological response. CONCLUSION: Responding to a request for hastened death can be an overwhelming task for clinicians. An approach that takes into consideration the legal, personal, professional and patient perspectives is required to provide a response that encompasses all the complexities associated with such a monumental request.
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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 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".