Medical assistance in dying in hospice: A qualitative study
Bibliographic record
Abstract
OBJECTIVES: The modern hospice movement has historically opposed assisted dying. The 2016 legalisation of medical assistance in dying (MAID) in Canada has created a new reality for Canadian hospices. There have been few studies examining how the legalisation of MAID has affected Canadian hospices. Our objective was to identify the challenges and opportunities hospice workers think MAID brings to a hospice. METHODS: This qualitative descriptive study included four focus groups and four semistructured interviews with Canadian hospice workers at two hospices, one which allowed MAID on site, and one which did not. Thematic analysis was used to understand and report these challenges and opportunities. RESULTS: We constructed five themes. These themes detailed participants' beliefs in the abilities of hospice care, and how they felt MAID challenged these abilities. Further, participants felt that MAID itself created challenging situations for patients and families, and that local policies and practices led to additional institutional challenges. Some participants also felt that allowing MAID in hospice provided opportunities for more extensive end-of-life options. CONCLUSIONS: The legalisation of MAID in Canada has created both challenges and opportunities for Canadian hospices. A balancing of these challenges and opportunities may provide a path for Canadian hospices to navigate their new reality. Increasing demand for MAID means that hospices are likely to continue to encounter requests for MAID, and should enact supports to ensure staff are able to manage these challenges and make best use of the opportunities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".