Learning about psychiatric aspects of medical assistance in dying: a pilot survey of self-perceived educational needs among assessors in a Canadian academic hospital
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD) was legalized in Canada in 2016, although it has been accessible as an end-of-life option in the province of Quebec since 2015. Before its implementation in clinical settings, few physicians had received formal training on requests assessments. New data indicate MAiD requesters have high rates of psychiatric comorbidities. Hence, assessment and management of psychiatric and psychosocial issues among MAiD requesters are important competencies to develop for assessors, although few training programs address them. The aim of our study was to explore physicians' self-perceived educational needs on psychiatric aspects related to MAiD in the province of Quebec. METHODS: We conducted a cross-sectional online survey and used a non-probability sampling design in one academic tertiary care center. A descriptive analysis was performed, and responders were compared on different variables. RESULTS: From twenty-five physician assessors, nineteen responded anonymously to an online survey (n=19). The findings of our pilot study revealed that participants felt highly competent in most psychiatric aspects at end-of-life and related to MAiD practice, except for psychotherapy and psychopharmacology as well as depression identification. Most indicated strong interest in further training. No statistical differences were found among responders regarding previous experience or training in palliative care. CONCLUSIONS: Based on our study, MAiD assessors reported high level of competency in managing psychiatric issues among requesters, but that they also expressed a strong desire for additional education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".