Bibliographic record
Abstract
Euthanasia is the deliberate administration of medications with the explicit intention of ending life, whereas physician assisted dying is the prescription or supply of drugs to enable the patient to end their own life.1 We use the term ‘medical assistance in dying’ (MAID) to refer to both. The rationale for MAID is usually based on two principles: Such individual rights however exist within a broader community, familial and societal context. Philosopher Daniel Callaghan stated: “Euthanasia is not a private matter of self-determination. It is an act that requires two people to make it possible and a complicit society to make it acceptable”.2 We present our perspective on the (often unacknowledged) implications of MAID for the family, physician and the healthcare system. A MEDLINE review was done with snowballing that is, following up reference lists from papers identified. Several factors drive patient requests for MAID, including the fear of becoming a burden to others, depression and feelings of hopelessness, loss of identity or dignity, feeling isolated, being tired of existence, and fear of uncertainty; it is of interest that the fear of pain more than pain itself was also a driver.3–5 These studies explored the concerns of those with a terminal illness who usually did not have access to MAID and were considering a hypothetical situation. Initial experience from the Dying with Dignity programme in the USA6 and the Medical Assistance in Dying programme in Canada7 find that those dying through MAID are predominantly motivated by fear of the loss of control and autonomy. Interestingly, most were educated, higher socioeconomic level Caucasians (>90%).7 It has been suggested that ‘Being …
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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 | 0.057 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.009 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".