Medical assistance in dying for mental illness: a complex intervention requiring a correspondingly complex evaluation approach
Bibliographic record
Abstract
Medical assistance in dying for mental illness as a sole underlying medical condition (MAiD MI-SUMC) is a controversial and complex policy in terms of psychosocial and ethical medical practice implications. We discuss the status of MAiD MI-SUMC in Canada and argue for the use of the UK Medical Research Council's framework on complex interventions in programme evaluations of MAiD MI-SUMC. It is imperative to carefully and rigorously evaluate the implementation of MAiD MI-SUMC to ensure an understanding of the multiple facets of implementation in contexts permeated by unique social, economic, cultural and historical influences, with a correspondingly diverse array of outcomes. This requires a complexity-informed programme evaluation focused on context-dependent mechanisms and stakeholder experiences, including patients, service providers and other people affected by the policy. It is also important to consider the economic impact on health and social welfare systems. Such evaluations can provide the data needed to guide evidence-informed decision-making that can contribute to safer implementation and refinement of MAiD MI-SUMC.
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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 | 0.048 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".