Analysis of the Medical Assistance in Dying Cases in Ontario: Understanding the Patient Demographics of Case Uptake in Ontario since the Royal Assent and Amendments of Bill C-14 in Canada
Bibliographic record
Abstract
On June 17, 2016, the Canadian government legalized medical assistance in dying (MAID) across the country by giving Royal Assent to Bill C-14. This Act made amendments to the Criminal Code and other Acts relating to MAID, allowing physicians and nurse practitioners to offer clinician-administered and self-administered MAID in conjunction with pharmacists being able to dispense the necessary medications. The eligibility criteria for MAID indicates that the individual 1) must be a recipient of publicly funded health services in Canada, 2) be at least 18 years of age, 3) be capable of health-related decision-making, and 4) has a grievous and irremediable medical condition. Because this is a new practice in Canadian health care, there are no published Canadian statistics on MAID cases to date, and this paper constitutes the first analysis of MAID cases in both the province of Ontario and Canada. Internationally, there are only a few jurisdictions with similar legislation already in place (US, the Netherlands, Belgium, Luxembourg, Switzerland, Columbia, Japan, and the United Kingdom). The published statistics on MAID cases from these jurisdictions were reviewed and used to establish the current global practices and demographics of MAID and will provide useful comparisons for Canada. This analysis will 1) outline the Canadian legislative approach to MAID, 2) provide an understanding of which patient populations in Ontario are using MAID and under what circumstances, and 3) determine if patterns exist between the internationally published MAID patient demographics and the Canadian MAID data. Selected patient demographics of the first 100 MAID cases in Ontario were reviewed and analyzed using anonymized data obtained from the Office of the Chief Coroner for Ontario so that an insight into the provision of MAID in Ontario could be obtained. Demographic factors such as age, sex, the primary medical diagnosis that prompted the request for MAID, the patient rationale for making a MAID request, the place where MAID was administered, the nature of MAID drug regimen used, and the status/specialty of medical personnel who administered the MAID drug regimen were analyzed. The analysis revealed that the majority of the first 100 MAID recipients were older adults (only 5.2% of patients were aged 35-54 years, with no younger adults between ages 18-34 years) who were afflicted with cancer (64%) and had opted for clinician-administered MAID (99%) that had been delivered in either a hospital (38.8%) or private residence (44.9%). Although the cohort was small, these Ontario MAID demographics reflect similar observations as those published internationally, but further analysis of both larger and annual case uptake in both Ontario and Canada will be conducted as the number of cases increases.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".