MAiD as a case study in evidence for policy making: An account of the CCA assessment on medical assistance in dying
Bibliographic record
Abstract
In December 2016, the Council of Canadian Academies (CCA) was asked by the Government of Canada to undertake an assessment on Medical Assistance in Dying (MAiD), following from Parliament’s passage of Bill C-14: An Act to amend the Criminal Code and to make related amendments to other Acts. The CCA was asked to undertake an assessment of the state of knowledge on three topics that Parliament excluded from C-14: requests for MAiD by mature minors, advance requests for MAiD, and requests for MAiD where a mental disorder is the sole underlying medical condition. Here, we describe the way that the CCA responded to the request from the Government of Canada using a multidisciplinary expert panel approach, how different forms of evidence were identified and used, the impact of the CCA assessment as part of the broader conversation occurring in Canada, and its implications for health leaders.
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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 | 0.199 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.059 | 0.050 |
| Scholarly communication | 0.038 | 0.018 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.024 | 0.028 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".