Bibliographic record
Abstract
Euthanasia and assisted suicide, both sometimes referred to as medical assistance in dying (MAiD), have in recent years become increasingly important concerns for public health and for end-of-life care. In a context of increased life expectancy, protracting illness trajectories before death and changing discourses around patient autonomy, dignity and ‘good death’, an increasing number of jurisdictions have legalised forms of MAiD, that is, acts in which the death of a seriously ill and suffering person is hastened by means of a lethal dose of drugs at this person’s request.1 Up until a decade ago, the number of jurisdictions that legally accepted some form of MAiD was limited to a handful of relatively small European countries (the Netherlands, Belgium, Switzerland) and one state in the USA (Oregon). In the last decade (ie, between 2009 and 2018), this was substantially expanded with three countries (Luxembourg, Colombia and Canada) and eight states (Washington, Vermont, Montana, California, Colorado, Hawaii and Washington DC in the USA and the Australian state of Victoria). This means that over 180 million people now live in a place where they can legally access MAiD. This increased public health relevance of MAiD also intensifies its importance within end-of-life care research. Up until now, research has particularly been limited to the early-adaption countries and states2 and, even there, a number of concerns regarding MAiD practice have not yet been studied adequately. At the same time, MAiD remains subject of fierce debate. From our impression of the current state of research regarding MAiD practices, a number of shortcomings and needs in research can be identified. Of these research gaps, we suggest at least the following five as most directly addressable …
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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 | 0.030 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".