How can we improve the experiences of patients and families who request medical assistance in dying? A multi-centre qualitative study
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying has been available in Canada for 5 years, but it is unclear which practices contribute to high-quality care. We aimed to describe patient and family perspectives of quality of care for medical assistance in dying. METHODS: We conducted a multi-centre, qualitative descriptive study, including face to face or virtual one-hour interviews using a semi-structured guide. We interviewed 21 english-speaking patients found eligible for medical assistance in dying and 17 family members at four sites in Canada, between November 2017 and September 2019. Interviews were de-identified, and analyzed in an iterative process of thematic analysis. RESULTS: We identified 18 themes. Sixteen themes were related to a single step in the process of medical assistance in dying (MAID requests, MAID assessments, preparation for dying, death and aftercare). Two themes (coordination and patient-centred care) were theme consistently across multiple steps in the MAID process. From these themes, alongside participant recommendations, we developed clinical practice suggestions which can guide care. CONCLUSIONS: Patients and families identified process-specific successes and challenges during the process of medical assistance in dying. Most importantly, they identified the need for care coordination and a patient-centred approach as central to high-quality care. More research is required to characterize which aspects of care most influence patient and family satisfaction.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".