The impact of coronavirus disease 2019 on medical assistance in dying
Bibliographic record
Abstract
PURPOSE OF REVIEW: The COVID-19 pandemic and measures to contain its impact are drastically altering end-of-life and grief experiences around the world, including the practice and experience of medical assistance in dying (MAiD). RECENT FINDINGS: Recent published literature on the impact of COVID-19 on MAiD can be described under the following categories: studies investigating the impact of COVID-19 on MAiD from the healthcare providers' perspective; studies investigating the impact of COVID-19 on MAiD from the patient/family perspective; and opinion papers that review the impact of COVID-19 on MAiD from a legal-ethical perspective. Most of these studies were either conducted in Canada or included mostly Canadian participants. SUMMARY: Recent published research on the impact of COVID-19 on MAiD highlights the tensions between COVID-19 restrictions and individual control over the circumstances of dying, and the resulting impact on patient and family suffering and on moral injury for their MAiD providers. These reports may help inform risk mitigation strategies for the current pandemic and future similar public health crises that acknowledge the value of humane, family-centered care at the end of life.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".