The ‘Means Available to Relieve Suffering’: Translating Medical Assistance in Dying Safeguards in Canadian Policy and Practice
Bibliographic record
Abstract
In Canada in 2021, people with non-life-limiting health conditions and disabilities became eligible for medical assistance in dying (MAiD). New legislative safeguards include a ninety-day assessment period and a requirement that health professionals engage with the person requesting MAiD about ‘means available to relieve their suffering’ (MARS). Government communications about the MARS safeguards emphasise distinct policy objectives, that we illustrate by analysing two texts. We then report on an ongoing study with health professionals involved in MAiD. In interviews, participants described supporting patients to imagine possibilities for feeling differently, creatively devising interventions, and actively connecting patients with (and in some cases bringing about) services and resources. Drawing on literature on front-line policy making we show how discourses of expert communication, care, and advocacy animate a specific translation of the MARS safeguards, one that recognises social and relational as well as deliberative autonomy, and reflects a range of MAiD policy goals.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.039 | 0.031 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".