Perspectives of Canadian health leaders on the relationship between medical assistance in dying and palliative and end-of-life care services: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD) was legalized in Canada in 2016, but coordination of MAiD and palliative and end-of-life care (PEOLC) services remains underdeveloped. We sought to understand the perspectives of health leaders across Canada on the relationship between MAiD and PEOLC services and to identify opportunities for improved coordination. METHODS: In this quantitative study, we purposively sampled health leaders across Canada with expertise in MAiD, PEOLC, or both. We conducted semi-structured interviews between April 2021 and January 2022. Interview transcripts were coded independently by 2 researchers and reconciled to identify key themes using content analysis. We applied the PATH framework for Integrated Health Services to guide data collection and analysis. RESULTS: We conducted 36 interviews. Participants expressed diverse views about the optimal relationship between MAiD and PEOLC, and the desirability of integration, separation, or coordination of these services. We identified 11 themes to improve the relationship between the services across 4 PATH levels: client-centred services (e.g., educate public); health operations (e.g., cultivate compassionate and proactive leadership); health systems (e.g., conduct broad and inclusive consultation and planning); and intersectoral initiatives (e.g., provide standard practice guidelines across health care systems). INTERPRETATION: Health leaders recognized that cooperation between MAiD and PEOLC services is required for appropriate referrals, care coordination, and patient care. They identified the need for public and provider education, standardized practice guidelines, relationship-building, and leadership. Our findings have implications for MAiD and PEOLC policy development and clinical practice in Canada and other jurisdictions.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".