Medical assistance in dying (MAiD): Ethical considerations for psychologists.
Bibliographic record
Abstract
Significant ethical challenges arise when mental health practitioners care for patients who seek to accelerate their own dying for rational medically valid reasons. Current and proposed laws provide for medical assistance in dying (MAiD) in several U.S. jurisdictions, all of Canada, and several other nations. Differing provisions of these laws complicate their utility for some patients who seek aid in dying. Some extant laws include roles that mental health professionals might play in assessing patients’ competence or capacity to consent, mental illness, or other cognitive and behavioral factors. Practitioners who choose to accept roles in the MAiD process must consider and resolve a number of ethical challenges including potential conflicts between and among laws, ethical standards, third-party requests, personal values, and patients’ wishes. These include becoming aware of patients who may wish to act independently to end their lives when MAiD laws might otherwise exclude them. Examples from actual cases and the resultant discussion will form a basis for exploration of the ethical and legal complexities confronted when psychologists become engaged in the process either intentionally or incidentally. The lead article (Koocher) is not intended to comprehensively address MAiD in all of its complexity but rather to trigger a thoughtful discussion among the accompanying commentaries.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.060 | 0.062 |
| Insufficient payload (model declined to judge) | 0.003 | 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".