Health care providers’ ethical perspectives on waiver of final consent for Medical Assistance in Dying (MAiD): a qualitative study
Bibliographic record
Abstract
BACKGROUND: With the enactment of Bill C-7 in Canada in March 2021, people who are eligible for medical assistance in dying (MAiD), whose death is reasonably foreseeable and are at risk of losing decision-making capacity, may enter into a written agreement with their healthcare provider to waive the final consent requirement at the time of provision. This study explored healthcare providers' perspectives on honouring eligible patients' request for MAiD in the absence of a contemporaneous consent following their loss of decision-making capacity. METHOD: A critical qualitative methodology, using a feminist ethics theoretical lens with its focus on power and relationality, was used to examine how socio-political and environmental contexts influenced healthcare providers' moral agency and perspectives. Semi-structured interviews were conducted with 30 healthcare providers (13 physicians, six nurse practitioners, nine nurses and two social workers) from across Canada who provide MAiD-related care. RESULTS: Themes identified include; (1) balancing personal values and professional responsibilities, (2) anticipating strengths and limitations of the proposed waiver of final consent amendment, (3) experiencing ethical influences on decisions to enter into written agreements with eligible patients, (4) recognizing barriers to the enactment of MAiD in the absence of a contemporaneous consent and (5) navigating the potential for increased risks and burden. DISCUSSION: To our knowledge, this is the first study in Canada to explore healthcare providers' perspectives on waiving the final consent for MAiD using a written agreement. Most participants supported expanding eligible people's access to MAiD following loss of capacity, as they believed it would improve the patients' comfort and minimize suffering. However, the lack of patients' input at the time of provision and related ethical and legal challenges may impact healthcare providers' moral agency and reduce some patients' access to MAiD. Providers indicated they would enter into written agreements to waive final consent for MAiD on a case-by-case basis. This study highlights the importance of organizational, legal and professional support, adequate resources, clear policies and guidelines for the safety and wellbeing of healthcare providers and to ensure equitable access to MAiD.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| 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".