Informing the Future of End-of-Life Care in Canada: Lessons\nfrom the Quebec Legislative Experience
Bibliographic record
Abstract
There have been numerous and challenging developments respecting endof-life care in Canada. In Quebec, political consensus and changes in public opinion led to the adoption of end-of-life care legislation. This paper discusses the context and foundation of that reform and reviews its content with the objective of informing the future of end-of-life care in Canada. In the first part of the paper I explore the balancing of the right to life and autonomy, with a focus on the approach chosen in Quebec by the Legal Experts Panel Report. In Part 11, I discuss Quebec's adoption of An Act Respecting End-of-Life Care, which recognized the precedence of the right to autonomy at the end of life and what it entails. I also highlight the differences between the approaches of Quebec and the Supreme Court in the Carter decision to show how Carter affects the future of the Quebec Act and, conversely, how Quebec's laws affect the development of federal or provincial and territorial laws as we saw with the adoption of legislation to amend the Criminal Code.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".