The criminalization of medical mistakes in Canada: a review
Bibliographic record
Abstract
The issue of health professionals facing criminal charges of manslaughter or criminal negligence causing death or grievous bodily harm as a result of alleged negligence in their professional practice was thrown into stark relief by the recent acquittal of four physicians accused of mismanaging Canada’s blood system in the early 1980s. Stories like these, as well as international reports detailing an increase in the numbers of physicians being charged with (and in some cases convicted of) serious criminal offences as the result of alleged negligence in their professional practice, have resulted in some anxiety about the apparent increase in the incidence of such charges and their appropriateness in the healthcare context. Whilst research has focused on the incidence, nature and appropriateness of criminal charges against health professionals, particularly physicians, for alleged negligence in their professional practice in the United Kingdom, the United States, Japan, and New Zealand, the Canadian context has yet to be examined. This article examines the Canadian context and how the criminal law is used to regulate the negligent acts or omissions of a health care professional in the course of their professional practice. It also assesses the appropriateness of such use. It is important at this point to state that the analysis in this article does not focus on those, fortunately few, cases where a health professional has intentionally killed his or her patients but rather when patients’ deaths or grievous injuries were allegedly as a result of that health professional’s negligent acts or omissions when providing health services to that patient.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.020 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".