When public health goes wrong : the history and ethics of public health errors
Bibliographic record
Abstract
This dissertation comprises three papers examining the historical, ethical, and social aspects of public health errors. My first paper explores how US health authorities responded to the discovery of the late health effects of radiation treatment. Based on the examination of multiple primary and secondary sources of evidence; archival research conducted at the National Archive in Washington, DC; and research conducted through media web-archives, I show how efforts by Michael Reese hospital in Chicago to locate and examine former patients (and the media attention these efforts attracted) led to a nationwide campaign by the National Cancer Institute (NCI) to warn those who underwent radiation treatment during childhood. My second paper investigates the ethics of evidence and post-market surveillance of pharmaceuticals in Canada. Drawing on philosophical discussions of inductive risk, the paper examines what evidence should have been sufficient for Health Canada (HC) to revise the misleading information that appeared in the product monograph for OxyContin. Given the stakes involved, I argue that a less strict standard of evidence would have been appropriate, yet HC in fact took the opposite course, insisting on a higher standard of evidence than it normally requires. The time it took for Health Canada to revise the monograph may have contributed to the prescription opioid epidemic in Canada. This paper also contributes to existing philosophical work by demonstrating that inductive risks in the post-approval stage are important and linked to pre-approval inductive risks. My third paper provides a new concept of public health errors—defined as acts of commission or omission, culpable or not, by public health officials, whose consequences for population health were clearly worse than those of an alternative that could have been chosen instead. This conception better corresponds to the task of public health, compared to policy failure literature, where achievement of political objectives is often used to measure success, and has practical and theoretical advantages. It also serves as a valuable analytical lens for understanding general mechanisms leading to public health errors, with utility for scholars who study policy errors as well as for public health actors interested in preventing them.
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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 | 0.134 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.213 |
| Scholarly communication | 0.033 | 0.038 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.034 | 0.044 |
| 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".