A National Survey of Canadian Psychiatrists’ Attitudes toward Medical Assistance in Death
Bibliographic record
Abstract
BACKGROUND: Bill C-14 allows for medical assistance in dying (MAID) for patients who have intolerable physical or psychological suffering that occurs in the context of a reasonably foreseeable death. In Canada, psychiatrist support for MAID on the basis of mental illness and beliefs influencing level of support are unknown. The objectives of this research were to 1) determine if psychiatrists are supportive of MAID under certain conditions and on the basis of mental illness and 2) determine what factors are related to psychiatrist support for MAID on the basis of mental illness. METHODS: This cross-sectional study was conducted among 528 psychiatrists in Canada using an online survey platform (February 19 to March 11, 2016). RESULTS: The response rate was 20.9% ( n = 528). Most psychiatrists supported the legalisation of MAID in some circumstances (72%); however, only 29.4% supported MAID on the basis of mental illness. Factors correlating with decreased support for MAID for mental illness were the belief that MAID for mental illness would change the psychiatrists' commitment to their patients through enduring suffering, having a personal faith, and having had past patients who would have received MAID for mental illness were it legal but instead went on to recover. INTERPRETATION: This study found that most psychiatrists do not support the legalisation of MAID for mental illness, despite being quite supportive of MAID in general. Objections seemed to be based upon concern for vulnerable patients, personal moral objections, and concern for the effect it would have on the therapeutic alliance.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".