Exploring key stakeholders’ attitudes and opinions on medical assistance in dying and palliative care in Canada: a qualitative study protocol
Bibliographic record
Abstract
INTRODUCTION: Canadians have had legal access to medical assistance in dying (MAiD) since 2016. However, despite substantial overlap in populations who request MAiD and who require palliative care (PC) services, policies and recommended practices regarding the optimal relationship between MAiD and PC services are not well developed. Multiple models are possible, including autonomous delivery of these services and formal or informal coordination, collaboration or integration. However, it is not clear which of these approaches are most appropriate, feasible or acceptable in different Canadian health settings in the context of the COVID-19 pandemic and in the post-pandemic period. The aim of this qualitative study is to understand the attitudes and opinions of key stakeholders from the government, health system, patient groups and academia in Canada regarding the optimal relationship between MAiD and PC services. METHODS AND ANALYSIS: A qualitative, purposeful sampling approach will elicit stakeholder feedback of 25-30 participants using semistructured interviews. Stakeholders with expertise and engagement in MAiD or PC who hold leadership positions in their respective organisations across Canada will be invited to provide their perspectives on the relationship between MAiD and PC; capacity-building needs; policy development opportunities; and the impact of the COVID-19 pandemic on the relationship between MAiD and PC services. Transcripts will be analysed using content analysis. A framework for integrated health services will be used to assess the impact of integrating services on multiple levels. ETHICS AND DISSEMINATION: This study has received ethical approval from the University Health Network Research Ethics Board (No 19-5518; Toronto, Canada). All participants will be required to provide informed electronic consent before a qualitative interview is scheduled, and to provide verbal consent prior to the start of the qualitative interview. Findings from this study could inform healthcare policy, the delivery of MAiD and PC, and enhance the understanding of the multilevel factors relevant for the delivery of these services. Findings will be disseminated in conferences and peer-reviewed publications.
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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 | 0.046 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".