Medical assistance in dying when natural death is not reasonably foreseeable
Bibliographic record
Abstract
OBJECTIVE: To describe clinicians' experiences with assessing patients making track 2 requests for medical assistance in dying (MAID) and providing MAID to such patients in the first 6 months after Canada amended relevant legislation in March 2021 to expand access to MAID. DESIGN: Online survey with closed and open-ended questions about clinicians' experiences with individual patients making track 2 MAID requests. SETTING: Canada. PARTICIPANTS: Doctors and nurse practitioners who were members of the Canadian Association of MAID Assessors and Providers. MAIN OUTCOME MEASURES: The most common reasons patients gave for making track 2 MAID requests and the challenges providers identified in doing these assessments. RESULTS: Twenty-three MAID providers submitted information about 54 patients who had made track 2 requests between March 17, 2021 and September 17, 2021. The most common diagnoses were chronic pain syndromes, affecting 28 patients (51.9%), and complex chronic conditions such as myalgic encephalomyelitis or chronic fatigue syndrome, affecting 8 patients (14.8%). The most common challenges providers reported were related to patients having concurrent mental illness, noted in 37 assessments (68.5%). In 8 cases (14.8%), providers faced challenges in finding experts to help with assessments. In 19 cases (35.2%), providers felt patients had not been offered all appropriate and available treatments, and in 9 cases (16.7%) providers encountered difficulties in finding such treatments for patients. CONCLUSION: Providers of MAID described many challenges in their experiences with patients making track 2 requests, including assessing individuals with concurrent mental illnesses, being uncertain that patients had been offered appropriate treatments prior to seeking MAID, and being unsure whether patients had seriously considered available treatments. Many providers experienced moral distress in attempting to balance patients' rights with what might be in patients' best interests. This is different from experiences providers have had with patients making track 1 requests, as most of these patients have end-stage malignancy or organ failure and seldom have unmet health care needs. This information could be used to enhance education and support for clinicians as they help patients with track 2 requests access their right to peaceful deaths.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".