How the experience of medical assistance in dying changed during the COVID-19 pandemic in Canada: a qualitative study of providers
Bibliographic record
Abstract
BACKGROUND: In March 2020, all levels of government introduced various strategies to reduce the impact of the COVID-19 pandemic. The purpose of this study was to document how the experience of providing medical assistance in dying (MAiD) changed during the COVID-19 pandemic. METHODS: We conducted a qualitative study using semistructured interviews with key informants in Canada who provided or coordinated MAiD before and during the COVID-19 pandemic. We interviewed participants from April to June 2020 by telephone or email. We collected and analyzed data in an iterative manner and reached theme saturation. Our team reached consensus on the major themes and subthemes. RESULTS: We interviewed 1 MAiD coordinator and 15 providers, including 14 physicians and 1 nurse practitioner. We identified 4 main themes. The most important theme was the perception that the pandemic increased the suffering of patients receiving MAiD by isolating them from loved ones and reducing available services. Providers were distressed by the difficulty of establishing rapport and closeness at the end of life, given the requirements for physical distancing and personal protective equipment. They were concerned about the spread of SARS-CoV-2, and found it difficult to enforce rules about distancing and the number of people present. Logistics and access to MAiD became more difficult because of the new restrictions, but there were many adaptations to solve these problems. INTERPRETATION: Providers and coordinators had many challenges in providing MAiD during the COVID-19 pandemic, including their perception that the suffering of their patients increased. Some changes in how MAiD is provided that have occurred during the pandemic, including more telemedicine assessments and virtual witnessing, are likely to remain after the pandemic and may improve service.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".