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Record W2990428384

The criminalisation of medical mistakes in Canada : a review

2008· review· en· W2990428384 on OpenAlexaboutno aff
Fiona McDonald

Bibliographic record

VenueFaculty of Law; Australian Centre for Health Law Research · 2008
Typereview
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsAcquittalContext (archaeology)HarmHealth careCriminologyLawMedicineProfessional conductPolitical sciencePsychologyHistory
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.414
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.588
GPT teacher head0.637
Teacher spread0.050 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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