Statutory Good-Faith Immunity for Government Physicians: Cogent Policy or a Denial of Justice?
Bibliographic record
Abstract
Recent events such as the SARS outbreak and the controversy over pediatric forensic pathology in Ontario have increased awareness and scrutiny of physicians employed by the government, including medical officers of health, coroners, and pathologists. At common law, physicians are held to a standard of care that can be summarized as reasonable professional competence. Statutory provisions effectively neutralize this standard of care for government physicians by providing civil immunity so long as they act in “good faith”. The appropriate-ness of this protection from civil liability is assessed in this paper.\nThe author argues that statutory good-faith immunity is inconsistent with the requirements that these positions be held by licensed doctors; indeed, it is a common provision of legislation for government employees that is not appropriate to the special case of government physicians. The Ontario statutory and case law is canvassed in relation to the powers and duties of coroners, forensic pathologists, and medical officers of health. It is then demonstrated that this statutory good-faith immunity is applied to the vast majority of public actors in Ontario. Within this context, the historic and current policy rationales for the immunity are assessed with reference to the recent judgments of the Supreme Court of Canada and the Ontario Court of Appeal establishing a tort of negligent investigation by police. The author then assesses how the common law of tort would apply to government physicians if these provisions were repealed.
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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 | 0.026 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.066 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.033 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".