Medical Aid in Dying (MAID): A Comparative Analysis of Domestic and International Approaches to This Controversial Subject
Bibliographic record
Abstract
This Note analyzes the Medical Aid in Dying (MAID) structures in various countries involving their eligibility criteria, application, and potential consequences. Section II analyzes MAID in the United States. Part A discusses the background and evolution of MAID laws in the United States on both a federal and state level. Part B is a multi-part analysis of the suggested expanded definition of terminal illness. Argument 1 evaluates the concept of the value of and respect for life; this is a threshold matter which will be the basis for the central arguments in this Note. Argument 2 explores the relationship between MAID laws and hospice care and how they affect one another; the definition of terminal illness in both circumstances must match each other to produce the best results. Argument 3 discusses the proposed “reasonable judgment” standard and how, although many believe it to be an objective standard, it is ultimately subjective and thus problematic. Argument 4 highlights the inaccuracies of diagnoses and the negative outcomes misdiagnoses may cause due to their effect on eligibility requirements. Section III then turns to examine MAID’s application internationally. Part A focuses on Switzerland’s nonprofit model and the consequences of increased costs, a lack of universal standards, and recent inventions in connection with MAID. Part B focuses on Canada and its liberal and broad application of MAID and its effects on those with physical and mental disabilities. Section IV briefly summarizes the arguments and poses additional questions to be considered.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".