Experiences of healthcare providers with eligible patients’ loss of decision-making capacity while awaiting medical assistance in dying
Bibliographic record
Abstract
Background: In Canada, under Bill C-14, patients who met all eligibility requirements were prevented from accessing medical assistance in dying (MAiD) following their loss of decision-making capacity while awaiting MAiD. The changes introduced with Bill C-7 continue to limit access to patients who did not enter a waiver of final consent agreement with their healthcare providers. Little is known about the experiences with patients' loss of capacity to consent and subsequent ineligibility for MAiD. Understanding healthcare providers' experiences has important implications for improving end-of-life care for those with capacity-limiting conditions. Purpose: To explore Canadian healthcare providers' experiences with end-of-life of eligible patients who became ineligible for MAiD due to their loss of decision-making capacity to consent and the relational influences on their experiences prior to the implementation of Bill C-7 in Canada. Method: A critical qualitative methodology and a feminist ethics theoretical lens guided this study. A voice-centred relational approach that allowed an in-depth exploration of how power, relationality and moral agency influenced participants' experiences was used for data analysis. Data consisted of semi-structured interviews with 30 healthcare providers. Findings: The analysis resulted in the following four main themes and corresponding subthemes: (1) identifying factors that may result in ineligibility for MAiD due to capacity loss; (2) maintaining eligibility required to access MAiD; (3) preparing for an alternative end-of-life; (4) experiencing patients' capacity loss. Discussion: This study highlights that while MAiD is legally available to eligible Canadians, access to MAiD and care for eligible patients who were unable to access MAiD due to their loss of decision-making varied based on the geographical locations and access to willing MAiD and end-of-life care providers. The availability of high-quality palliative care for patients throughout the MAiD process, including following the loss of capacity to consent and subsequent ineligibility, would improve the end-of-life experience for all those involved. The need to establish a systematic approach to prepare and care for patients and their families following the patients' loss of capacity and subsequent ineligibility for MAiD is also identified.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".