Bibliographic record
Abstract
PURPOSE OF REVIEW: Medical Assistance in Dying (MAID) is now legal in many jurisdictions for competent adults who have intolerable suffering and/or have a terminal illness with a short prognosis. Mental illness can be a source of suffering for these individuals, but it can also affect their capacity to make medical decisions. Clinicians, and psychiatrists in particular, need to understand how to assess patients with mental illness who are requesting MAID, to determine the impact of their mental illness on the MAID request. RECENT FINDINGS: Psychiatric disorders can be a primary indication for MAID in parts of Europe, and recent published case series from Belgium and the Netherlands have generated strong responses from the psychiatric community. Patients dying of terminal illnesses who request MAID often have symptoms of depression or anxiety, but psychiatrists are rarely involved in their care. Psychiatrists may be helpful in assessing decision capacity, but documentation of capacity assessment could be improved. There is a broad need to develop educational resources to train current and future physicians about MAID. SUMMARY: MAID represents an ethical and clinical challenge for psychiatrists in a variety of ways. As more jurisdictions legalize MAID, the psychiatric community will need to be prepared to meet these challenges with robust clinical standards and educational programs to ensure the highest standards of care for patients.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".