WHO guidance on ethics in outbreaks and the COVID-19 pandemic: a critical appraisal
Bibliographic record
Abstract
In 2016, following pandemic influenza threats and the 2014-2016 Ebola virus disease outbreaks, the WHO developed a guidance document for managing ethical issues in infectious disease outbreaks. In this article, we analyse some ethical issues that have had a predominant role in decision making in response to the current COVID-19 pandemic but were absent or not addressed in the same ways in the 2016 guidance document. A pandemic results in a health crisis and social and political crises both nationally and globally. The ethical implications of these global effects should be properly identified so that appropriate actions can be taken globally and not just in national isolation. Our analysis, which is a starting point to test the broader relevance of the 2016 WHO document that remains the only available guidance document applicable globally, concludes that the WHO guidance should be updated to provide reasoned and thoughtful comprehensive ethics advice for the sound management of the current and future pandemics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.368 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.016 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".