Assessment of capacity to give informed consent for medical assistance in dying: a qualitative study of clinicians’ experience
Bibliographic record
Abstract
BACKGROUND: , medical assistance in dying (MAiD) requires that patients give informed consent and that their ability to consent is assessed by 2 clinicians. In this study, we intended to understand how Canadian clinicians assessed capacity in people requesting MAiD. METHODS: This qualitative study used interviews conducted between August 2019 and February 2020, by phone, video and email, to explore how clinicians assessed capacity in people requesting MAiD, what challenges they had encountered and what tools they used. The participants were recruited from provider mailing listserves of the Canadian Association of MAiD Assessors and Providers and Aide médicale à mourir. Interviews were audio-recorded and transcribed verbatim. The research team met to review transcripts and explore themes as they emerged in an iterative manner. We used abductive reasoning for thematic analysis and coding, and continued to discuss until we reached consensus. RESULTS: The 20 participants worked in 5 of 10 provinces across Canada, represented different specialties and had experience assessing a total of 2410 patients requesting MAiD. The main theme was that, for most assessments, the participants used the conversation about how the patient had come to choose MAiD to get the information they needed. When the participants used formal capacity assessment tools, this was mostly for meticulous documentation, and they rarely asked for psychiatric consults. The participants described how they approached assessing cases of nonverbal patients and other challenging cases, using techniques such as ensuring a quiet environment and adequate hearing aids, and using questions requiring only "yes" or "no" as an answer. INTERPRETATION: The participants were comfortable doing MAiD assessments and used their clinical judgment and experience to assess capacity in ways similar to other clinical practices. The findings of this study suggest that experienced MAiD assessors do not routinely require formal capacity assessments or tools to assess capacity in patients requesting MAiD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".