“Architects’ mistakes should be covered with ivy and doctors’ with sod”: Medical Malpractice, Morton Shulman, and the “Conspiracy of Silence”
Bibliographic record
Abstract
Every year thousands of Canadians are killed or injured because of medical mistakes. Plaintiffs, however, face several challenges to suing doctors successfully for malpractice. One challenge is the reluctance of some doctors to criticize other medical professionals, sometimes referred to as the “conspiracy of silence.” This article focuses on the attention given to the conspiracy of silence in Ontario in the 1960s when Dr. Morton Shulman, the pugnacious and flamboyant Chief Coroner of Toronto, alleged that doctors routinely covered up medical errors. Shulman drew media attention to irresponsible doctors, poor practice, and negligent treatment. He demanded more accountability and better care, and deplored efforts to silence him to protect the reputation of doctors. A decline in public trust of experts and of the professions created conditions that lent credence to Shulman’s claims. However, many medical professionals chafed at the questioning of their professionalism, expertise, and ethics. The provincial government’s responses, which included coroner system reforms, expanding the powers of the Ontario College of Physicians and Surgeons, and attempting to silence Shulman, primarily aimed to meet the concerns of the state and medical professionals, rather than those of patients and the public.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".