Communication is Key: A Qualitative Study on the Experience of Medical Assistance in Dying (MAiD) Communication for Patients with Advanced Cancer
Bibliographic record
Abstract
Canada’s legalization of Medical Assistance in Dying (MAiD) offered a new end-of-life option. Yet, clinical communication guidelines are not evidence-based and patient conversations throughout the MAiD process with healthcare providers and family/friends remain unknown. This study aimed to better understand the patient experience of MAiD communication, and the nature of these conversations. Twenty patients with advanced cancer from a Canadian comprehensive cancer centre participated via semi-structured interviews. Transcripts were analyzed using constructivist grounded theory methodology. The core category “Experiencing the complexities and ambiguities of MAiD communication” symbolizes its dynamic and uncertain nature, where patients are questioning and planning MAiD conversations. The experience is detailed across four subcategories representing the Four W’s: WHAT are the end-of-life options, WHO to discuss MAiD with, WHEN are MAiD conversations initiated, and WHY discuss a patient’s reasons for MAiD. The theory can help inform future MAiD communication protocols and support optimal end-of-life care for patients.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".