The Expansion of Medical Assistance in Dying in the COVID-19 Pandemic Era and Beyond
Bibliographic record
Abstract
In 2015, the Canadian parliament passed a law permitting adults to request Medical Assistance in Dying (MAiD) when they have a grievous, irremediable medical condition that causes unbearable suffering and their natural death is reasonably foreseeable. Following a constitutional challenge, a Quebec lower court, ruled in the Truchon vs. Canada AG case that the restriction to a reasonably foreseeable death is an unjustifiable impingement on the right to life, liberty, and security of the person and the right to equality. In response, the government expanded the MAiD law in March 2021 through Bill C-7 to include those who are not approaching their natural death. Bill C-7 is a potentially harmful approach to justice for vulnerable groups such as the elderly, disabled, or those with chronic illnesses. The COVID-19 pandemic has highlighted serious problems with how we care for the vulnerable members of our society. In this article, we explore what has gone wrong and what has raised serious concerns, while proposing potential options to consider when developing new laws, systems, and processes to improve societal equity.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".