Medical Assistance in Dying: Challenges of Monitoring the Canadian Program
Bibliographic record
Abstract
The Canadian medical assistance in dying (MAID) program, based on an ambitious piece of legislation and detailed regulations, has failed to provide Canadians with sufficient publicly accessible evidence to show that it is operating as mandated by the requirements of the law, regulations, and expectations of all stakeholders. The federal law that was adopted in 2016 defined the eligibility criteria and put in place a number of safeguards that had to be satisfied before providing assisted dying to a person in order not to transgress the Criminal Law. The responsibility of monitoring for the purpose of investigating compliance with the eligibility criteria and procedural safeguards was assigned by the Federal Ministry of Health (responsible for all monitoring) to the provincial and territorial governments. Some of the governments have released statistical data concerning the program, but none have yet issued a comprehensive report on adherence to the eligibility criteria and its safeguards as required by the law and regulations. This paper explains the process, explores the possible reasons for this shortfall, and offers some suggestions for actions that could rectify this aspect of the MAID program. Accountability and transparency are integral to the delivery of MAID and the publications of the mandated federal as well as provincial/territorial monitoring reports are one important approach to achieving confidence and trust in the program.
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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 | 0.128 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".