The impact of the COVID-19 pandemic on medical assistance in dying in Canada and the relationship of public health laws to private understandings of the legal order
Bibliographic record
Abstract
Drawing on interviews we conducted with 15 medical assistance in dying (MAiD) providers from across Canada, we examine how physicians and nurse practitioners reconcile respect for the new, changing rules brought upon by the coronavirus disease 2019 (COVID-19) pandemic, along with their existing legal obligations and ethical commitments as health care professionals and MAiD providers. Our respondents reported situations where they did not follow or did not insist on others following the applicable public health rules. We identify a variety of techniques that they deployed either to minimize, rationalize, justify or excuse deviations from the relevant public health rules. They implicitly invoked the exceptionality and emotionality of the MAiD context, especially in the time of COVID, when offering their accounts and explanations. What respondents relate about their experiences providing MAiD during the COVID pandemic offers occasion to reflect on the role actors themselves play in giving meaning (if not coherence) to the potentially conflicting normative expectations to which they are subject.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.036 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| 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".