The ethics of sharing preliminary research findings during public health emergencies: a case study from the 2009 influenza pandemic
Bibliographic record
Abstract
During the 2009 A(H1N1) influenza pandemic, a suite of studies conducted in Canada showed an unexpected finding, that patients with medically attended laboratory-confirmed pandemic influenza were more likely to have received seasonal influenza vaccination than test-negative control patients. Different bodies, including scientific journals and government scientific advisory committees, reviewed the evidence simultaneously to determine its scientific validity and implications. Decision-making was complicated when the findings made their way into the media. The normal trajectory of non-urgent research includes peer-review publication after which decision-makers can process the information taking into account other evidence and logistic considerations. In the situation that arose, however, the congruence of an unexpected finding and the simultaneous review of the evidence both within and outside the traditional peer-review sphere raised several interesting issues about how to deal with emerging evidence during a public health emergency. These events are used in this article to aid discussion of the complex interrelationship between researchers, public health decision-makers and scientific journals, the trade-offs between sharing information early and maintaining the peer-review quality assurance process, and to emphasise the need for critical reflection on the practical and ethical norms that govern the way in which research is evaluated, published and communicated in public health emergencies.
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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 | 0.269 | 0.405 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.032 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".