Patient experiences with requests for medical assistance in dying
Bibliographic record
Abstract
OBJECTIVE: To explore experiences of patients who have complex chronic conditions (CCCs), such as fibromyalgia and chronic fatigue syndrome, when they request medical assistance in dying (MAID) in Canada. DESIGN: Qualitative study using semistructured interviews. SETTING: Canada. PARTICIPANTS: Individuals with CCCs who had contacted any 1 of 4 advocacy organizations between January 21, 2021, and December 20, 2022, about requesting MAID for suffering related to CCCs or who had applied and been assessed for MAID. METHODS: Interviews were conducted virtually (by video or audio) and recordings were transcribed. Thematic analysis was conducted in an iterative manner with abductive analysis. As interviews were completed, transcripts were reviewed and emerging themes were discussed at regular intervals. MAIN FINDINGS: Sixteen individuals were interviewed. All spoke of long-lasting suffering that was unresponsive to an array of medical treatments. Although some participants had hoped to receive MAID immediately following the 90-day assessment period, many mentioned that approval would provide or had provided validation of their illness and a sense of control, especially should their illness become unbearable. Participants sharply distinguished between MAID and suicide, saying they preferred MAID because it offered greater certainty and caused less emotional pain to others. Many said that participating in this research was beneficial because they believed the interviewers truly listened to them. CONCLUSION: Participants described experiences with CCCs and requests for MAID. This information may provide family doctors with new insight to inform interactions with patients with CCCs.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".