Early experience with medical assistance in dying in Ontario, Canada: a cohort study
Bibliographic record
Abstract
BACKGROUND: Medical assistance in dying (MAiD) was legalized across Canada in June 2016. Some have expressed concern that patient requests for MAiD might be driven by poor access to palliative care and that social and economic vulnerability of patients may influence access to or receipt of MAiD. To examine these concerns, we describe Ontario's early experience with MAiD and compare MAiD decedents with the general population of decedents in Ontario. METHODS: We conducted a retrospective cohort study comparing all MAiD-related deaths with all deaths in Ontario, Canada, between June 7, 2016, and Oct. 31, 2018. Clinical and demographic characteristics were collected for all MAiD decedents and compared with those of all Ontario decedents when possible. We used logistic regression analyses to describe the association of demographic and clinical factors with receipt of MAiD. RESULTS: A total of 2241 patients (50.2% women) were included in the MAiD cohort, and 186 814 in the general Ontario decedent cohort. Recipients of MAiD reported both physical (99.5%) and psychologic suffering (96.4%) before the procedure. In 74.4% of cases, palliative care providers were involved in the patient's care at the time of the MAiD request. The statutory 10-day reflection period was shortened for 26.6% of people. Compared with all Ontario decedents, MAiD recipients were younger (mean 74.4 v. 77.0 yr, standardized difference 0.18);, more likely to be from a higher income quintile (24.9% v. 15.6%, standardized difference across quintiles 0.31); less likely to reside in an institution (6.3% v. 28.0%, standardized difference 0.6); more likely to be married (48.5% v. 40.6%) and less likely to be widowed (25.7% v. 35.8%, standardized difference 0.34); and more likely to have a cancer diagnosis (64.4% v. 27.6%, standardized difference 0.88 for diagnoses comparisons). INTERPRETATION: Recipients of MAiD were younger, had higher income, were substantially less likely to reside in an institution and were more likely to be married than decedents from the general population, suggesting that MAiD is unlikely to be driven by social or economic vulnerability. Given the high prevalence of physical and psychologic suffering, despite involvement of palliative care providers in caring for patients who request MAiD, future studies should aim to improve our understanding and treatment of the specific types of suffering that lead to a MAiD request.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".