‘My Advocacy is Not About Me, My Advocacy is About Canadians’: A Qualitative Study of how Caregivers and Patients Influence Regulation of Medical Assistance in Dying in Canada
Bibliographic record
Abstract
Medical assistance in dying (MAiD) was legalised federally in Canada after the Supreme Court decision in Carter v Canada (Attorney General) [2015] 1 SCR 331. The federal legislative framework for MAiD was established via Bill C-14 in 2016. Caregivers and patients were central to Carter and subsequent litigation and advocacy, which resulted in amendments to the law via Bill C-7 in 2021. Research has primarily focused on the impacts of regulation on caregivers and patients. This qualitative study investigates how caregivers and patients influence law reform and the operation of MAiD practice in Canada (ie, behave as 'regulatory actors'), using Black's definition of regulation. We found that caregivers and patients performed sustained, focused, and intentional actions that influenced law reform and the operation of MAiD in practice. Caregivers and patients are not passive objects of Canadian MAiD regulation, and their role in influencing regulation (eg, law reform and MAiD practice) should be supported where this is desired by the person. However, recognising the burdens of engaging in regulatory action to address barriers to accessing MAiD or to quality care, and MAiD system gaps, other regulatory actors (eg, governments) should minimise this burden, particularly where a person engages in regulatory action reluctantly.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.028 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".