How can we improve the experiences of patients and families who request medical assistance in dying? A multi-centre qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it