Medical Assistance in Dying (MAiD) for Canadian Prisoners: A Case Series of Barriers to Care in Completed MAiD Deaths
Bibliographic record
Abstract
Background: As of August 2020, 11 patients who were federally incarcerated in a Canadian prison requested medical assistance in dying (MAiD), and three received it. This case study seeks to understand the process of care as described by physicians involved in each of the cases that resulted in MAiD. Methods: During the summer of 2020, semistructured interviews were conducted with physicians involved in each known Correctional Service of Canada (CSC) MAiD case. Transcripts were summarized to illuminate details of the care process for each patient, highlighting barriers to patient-centered care. Results: Each case took place in a different province. One MAiD provision took place in a prison hospital, and two provisions took place after the incarcerated patients were transferred to external community hospitals. Case summaries highlight the physicians' efforts and challenges in assuring patient-centered care. Discussion: Physician experiences illuminate several barriers to care: CSC bureaucratic processes that forced longer wait times than typical for patients in the general public; challenges related to accessing release before MAiD application; knowledge of patient preference for location of death; concerns of voluntariness and confidentiality that are unique to CSC patients; and ethical considerations surrounding the presence of prison guards, police officers, and shackles at the time of assessment or provision. Reporting by the Office of the Correctional Investigator highlights additional challenges in these cases. Further inquiry is necessary to include the perspectives of prisoners and prison staff, and to consider how the evolution of new MAiD legislation will affect MAiD for prisoners.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".