Navigating medical assistance in dying from Bill C-14 to Bill C-7: a qualitative study
Bibliographic record
Abstract
Background Even as healthcare providers and systems were settling into the processes required for Medical Assistance in Dying (MAID) under Bill C-14, new legislation was introduced (Bill C-7) that extended assisted death to persons whose natural death is not reasonably foreseeable. The purpose of this paper is to describe the experiences of nurses and nurse practitioners with the implementation and ongoing development of this transition. Methods This qualitative longitudinal descriptive study gathered data through semi-structured telephone interviews with nurses from across Canada; cross sectional data from 2020 to 2021 is reported here. The study received ethical approval and all participants provided written consent. Findings Participants included nurses (n = 34) and nurse practitioners (n = 16) with significant experience with MAID. Participants described how MAID had transitioned from a new, secretive, and anxiety-producing procedure to one that was increasingly visible and normalized, although this normalization did not necessarily mitigate the emotional impact. MAID was becoming more accessible, and participants were learning to trust the process. However, the work was becoming increasingly complex, labour intensive, and often poorly remunerated. Although many participants described a degree of integration between MAID and palliative care services, there remained ongoing tensions around equitable access to both. Participants described an evolving gestalt of determining persons’ eligibility for MAID that required a high degree of clinical judgement. Deeming someone ineligible was intensely stressful for all involved and so participants had learned to be resourceful in avoiding this possibility. The required 10-day waiting period was difficult emotionally, particularly if persons worried about losing capacity to give final consent. The implementation of C-7 was perceived to be particularly challenging due to the nature of the population that would seek MAID and the resultant complexity of trying to address the origins of their suffering within a resource-strapped system. Conclusions Significant social and system calibration must occur to accommodate assisted death as an end-of-life option. The transition to offering MAID for those whose natural death is not reasonably foreseeable will require intensive navigation of a sometimes siloed and inaccessible system. High quality MAID care should be both relational and dialogical and those who provide such care require expert communication skills and knowledge of the healthcare system.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".