Medical Assistance in Dying (MAiD): A Descriptive Study From a Canadian Tertiary Care Hospital
Bibliographic record
Abstract
BACKGROUND: In June 2016, the Government of Canada passed Bill C-14 decriminalizing medically assisted death. Increasing numbers of Canadians are accessing medical assistance in dying (MAiD) each year, but there is limited information about this population. OBJECTIVE: To describe the characteristic outcomes of MAiD requests in a cohort of patients at an academic tertiary care center in Toronto, Ontario, Canada. METHODS: A retrospective chart review of patients making a formal request for a MAiD eligibility assessment from July 16 to September 18. Data extracted included demographics, diagnosis, psychosocial characteristics, information relating to the MAiD request, and clinical outcome. RESULTS: We received 107 formal requests for MAiD assessment. Ninety-seven patients were found eligible, of whom 80 received MAiD. Cancer was the primary diagnosis for 78% and median age was 74 years. The majority of patients (64%) cited "functional decline or inability to participate in meaningful activities" as the main factor motivating their request for MAiD. Half of patients who received MAiD (46%) described their request as consistent with a long-standing, philosophical view predating their illness. The 10-day reflection period was reduced for 39% of provisions due to impending loss of capacity. Our cohort was very similar demographically to those described both nationally and internationally. CONCLUSION: Patients seeking MAiD at our institution were similar to those described in other jurisdictions where assisted dying is legal and represent a group for whom autonomy and independence is critical. We noted a very high rate of risk of loss of capacity, suggesting a need for both earlier assessments and regular monitoring.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".