Bibliographic record
Abstract
Legalizing medical assistance in dying (MAiD) has transformed how palliative and hospice care providers (PHCPs) engage in end of life conversations with patients and their loved ones. We do not currently know how PHCPs in the Toronto area have experienced this tremendous shift, and what challenges they have faced in transforming their practice to accommodate MAiD conversations. We must understand the unique challenges faced by PHCPs so that we can improve the education, resources, and supports available to these essential healthcare providers. We also must ensure that Canadians receive the highest quality care at the end of life, whether or not they choose to pursue a medically assisted death. To examine these unique challenges, I interviewed 22 PHCPs to document their experiences of engaging in end-of-life conversations in the post-MAiD era. Participants included physicians, nurses, social workers, and other allied health professionals employed in faith-based and secular institutions. Their personal beliefs about MAiD varied widely; some identified as conscientious objectors, while others actively engaged in MAiD assessment and provision. Initial thematic analysis revealed that challenges include translating the federal legislation into medical practice; navigating inefficient institutional policies and role ambiguity; developing conversation techniques to share MAiD information with patients and families in a balanced way that is informative yet uncoercive; and, navigating the ethical and organizational issues that arise when patients with declining capacity pursue MAiD. Finally, PHCPs shared personal experiences of burnout, emotional weight, and stigma. Understanding how these factors impact the work and lives of PHCPs allows us to develop targeted strategies to improve the institutional policies surrounding MAiD conversations, referrals, and procedures, as well as decrease the negative personal and emotional consequences of engaging in end-of-life conversations in the post-MAiD era.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".