Bibliographic record
Abstract
Abstract Research conducted during infectious disease outbreaks or pandemics can be crucial to control or ameliorate their consequences, but scientists are confronted with significant ethical questions about how to conduct research in such contexts. This chapter examines foundational ethical questions and considerations undergirding the research enterprise in pandemic contexts, including whether pandemics necessitate deviations from ethical and scientific standards for research, how research priorities are and ought to be set during pandemics, the ethics of conducting research alongside pandemic response efforts, and how pandemic research ought to be governed and coordinated. Scientists may have only a brief interval to understand the disease and develop medical countermeasures, and social pressures to produce fast results may seem overwhelming. Despite these challenges, this does not justify relaxing fundamental ethical or scientific standards, although there is scope for accelerating procedural requirements. Even during a pandemic, provision of most biomedical research funding by high-resource countries can influence the directions and results of research, leading to interventions that are more applicable in resource-rich than in resource-poor countries. The World Health Organization and other institutions are trying to correct or at least reduce these discrepancies. Biomedical research and health care response to a pandemic need not be rivals for funding support. The 2014 Ebola and 2019 COVID-19 experiences have demonstrated that the two can work synergistically. Both medical care and research responses are integral to our defenses against emerging or re-emerging infectious disease. Finally, the COVID-19 pandemic has made clear the need for changes in global architecture for pandemic response. The scientific and organizational adaptations required must be guided by ethical principles, such as the need to reduce the glaring inequities between high-income and low-income settings across the world as well as within countries. Preparing for the next pandemic will require a blueprint to accelerate the organization, coordination, and conduct of critical research and development.
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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 | 0.097 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.050 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".