Loss of Capacity to Consent and Access to Medical Assistance in Dying: Healthcare Providers’ Experiences and Perspectives
Bibliographic record
Abstract
Under Bill C-14, patients who met eligibility requirements were prevented from accessing medical assistance in dying (MAiD) if they lost decision-making capacity while awaiting MAiD. Little is known about healthcare providers’ experiences of patients’ loss of capacity to consent and subsequent ineligibility for MAiD. The enactment of Bill C-7 in 2021 has allowed eligible patients to use a waiver of final consent agreement to access MAiD following their loss of capacity, but healthcare providers’ perspectives on providing MAiD using the proposed amendments were not known. Therefore, the aims of this study were: 1) to explore Canadian healthcare providers’ perspectives on providing MAiD to eligible patients in the absence of a contemporaneous final consent; and 2) to explore healthcare providers’ experiences when their previously eligible patients were unable to access MAID due to a loss of capacity to consent. A critical qualitative methodology, informed by feminist ethics, was used focusing on the concepts of power, relationality, and moral agency. A voice-centred relational approach was used for data analysis. Semi-structured interviews were conducted with a heterogenous sample of 30 participants, (physicians, nurses, nurse practitioners and social workers) recruited using purposeful and snowball methods, who had experiences with patients’ loss of capacity and subsequent ineligibility for MAiD. The findings highlight that healthcare providers’ agency and experiences with MAiD were influenced by socio-political, personal, and professional factors, that in turn, had significant impact on their patients’ access to and experiences with MAiD. While participants morally supported the amendment to waive the final consent requirement, they anticipated ethical and legal challenges in the absence of patients’ contemporaneous consent. The study identifies gaps in the care and support offered to patients and families following patients’ capacity loss and subsequent ineligibility for MAiD. The inconsistencies and disparities in the access to MAiD and other end-of-life services across Canada also came to light. Organizational and professional support, adequate resources, and infrastructure as well as clear policies and guidelines are required to prevent capacity loss-related ineligibility for MAiD, to improve end-of-life care for incapacitated patients and their families, and to promote the safety and well-being of healthcare providers.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".