R&D during public health emergencies: the value(s) of trust, governance and collaboration
Bibliographic record
Abstract
In January 2021, Dr Tedros Adhanom Ghebreyesus, director-general of the WHO, warned that the world was 'on the brink of a catastrophic moral failure [that] will be paid with lives and livelihoods in the world's poorest countries'. We are now past the brink. Many high-income countries have vaccinated their populations (which, in some cases, includes third and even fourth doses) and are loosening public health and social measures, while low-income and middle-income countries are struggling to secure enough supply of vaccines to administer first doses. While injustices abound in the deployment and allocation of COVID-19 vaccines, therapies and diagnostics, an area that has hitherto received inadequate ethical scrutiny concerns the upstream structures and mechanisms that govern and facilitate the research and development (R&D) associated with these novel therapies, vaccines and diagnostics. Much can be learnt by looking to past experiences with the rapid deployment of R&D in the context of public health emergencies. Yet, much of the 'learning' from past epidemics and outbreaks has largely focused on technical or technological innovations and overlooked the essential role of important normative developments; namely, the importance of fostering multiple levels of trust, strong and fair governance, and broad research collaborations. In this paper, we argue that normative lessons pertaining to the conduct of R&D during the 2014-2016 Ebola epidemic in West Africa provide important insights for how R&D ought to proceed to combat the current COVID-19 pandemic and future infectious disease threats.
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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 | 0.222 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.066 |
| Scholarly communication | 0.035 | 0.023 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.017 | 0.022 |
| 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".