Canadian neurosurgeons’ views on medical assistance in dying (MAID): a cross-sectional survey of Canadian Neurosurgical Society (CNSS) members
Bibliographic record
Abstract
BACKGROUND: The Supreme Court of Canada removed the prohibition on physicians assisting in patients dying on 6 February 2015. Bill C-14, legalising medical assistance in dying (MAID) in Canada, was subsequently passed by the House of Commons and the Senate on 17 June 2016. As this remains a divisive issue for physicians, the Canadian Neurosurgical Society (CNSS) has recently published a position statement on MAID. METHODS: We conducted a cross-sectional survey to understand the views and perceptions among CNSS members regarding MAID to inform its position statement on the issue. Data was collected from May to June 2016. RESULTS: Of the 300 active membes of the CNSS who recevied the survey, 89 respondents completed the survey, 71% of whom were attending neurosurgeons and 29% were neurosurgery residents. Most respondents,74.2%, supported the right of physicians to participate in MAID with 7.8% opposing. 37% had current patients in their practice fitting the criteria for MAID. 23.6% had been asked by patients to assist with MAID, but only 11% would consider personally providing it. 84% of neurosurgeons surveyed supported the physicians' right to conscientious objection to MAID while 21% thought attending surgeons should be removed from the inquiry and decision-making process. 43.8% agreed that the requirment to refer a patient to a MAID service should be mandatory. Glioblastoma multiforme (65%), quadriplegia/quadriparesis secondary to spinal tumour/trauma (54%) and Parkinson's disease (24%) were the most common suggested potential indications for MAID among the neurosurgical population. CONCLUSIONS: Our results demonstrate that most neurosurgeons in Canada are generally supportive of MAID in select patients. However, they also strongly support the physicians' right to conscientious objection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".