Survey of Mental Health Care Providers’ Perspectives on the Everyday Ethics of Medical-Aid-in-Dying for People with a Mental Illness
Bibliographic record
Abstract
Context : In most jurisdictions where medical-aid-in-dying (MAiD) is available, this option is reserved for individuals suffering from incurable physical conditions. Currently, in Canada, people who have a mental illness are legally excluded from accessing MAiD. Methods : We developed a questionnaire for mental health care providers to better understand their perspectives related to ethical issues in relation to MAiD in the context of severe and persistent suffering caused by mental illness. We used a mixed-methods survey approach, using a concurrent embedded model with both closed and open-ended questions. Findings : 477 healthcare providers from the province of Québec (Canada) completed the questionnaire. One third of the sample (34.4%) were nurses, one quarter psychologists (24.3%) and one quarter psycho-educators (24%). Nearly half of the respondents (48.4%) considered that people with a severe mental illness should be granted the right to opt for MAiD as a way to end their suffering. Respondents were more likely to feel comfortable listening to the person and participating in discussions related to MAiD for a mental illness than offering care or the means for the person to access MAiD. Most (86.2%) reported that they had not received adequate/sufficient training, education or preparation in order to address ethical questions surrounding MAiD. Conclusions : The findings highlight how extending MAiD to people with a mental illness would affect daily practices for mental healthcare providers who work directly with people who may request MAiD. The survey results also reinforce the need for adequate training and professional education in this complex area of care.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".