Canada’s Medical Assistance in Dying System can Enable Healthcare Serial Killing
Bibliographic record
Abstract
The Canadian approach to assisted dying, Medical Assistance in Dying (MAiD), as of early 2024, is assessed for its ability to protect patients from criminal healthcare serial killing (HSK) to evaluate the strength of its safeguards. MAiD occurs through euthanasia or self-administered assisted suicide (EAS) and is legal or considered in many countries and jurisdictions. Clinicians involved in HSK typically target patients with the same clinical features as MAiD-eligible patients. They may draw on similar rationales, e.g., to end perceived patient suffering and provide pleasure for the clinician. HSK can remain undetected or unconfirmed for considerable periods owing to a lack of staff background checks, poor surveillance and oversight, and a failure by authorities to act on concerns from colleagues, patients, or witnesses. The Canadian MAiD system, effectively euthanasia-based, has similar features with added opportunities for killing afforded by clinicians' exemption from criminal culpability for homicide and assisted suicide offences amid broad patient eligibility criteria. An assessment of the Canadian model offers insights for enhancing safeguards and detecting abuses in there and other jurisdictions with or considering legal EAS. Short of an unlikely recriminalization of EAS, better clinical safeguarding measures, standards, vetting and training of those involved in MAiD, and a radical restructuring of its oversight and delivery can help mitigate the possibility of abuses in a system mandated to accommodate homicidal clinicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".