Regulator experiences of trials during Ebola epidemics in Sierra Leone, Guinea, and the Democratic Republic of the Congo
Bibliographic record
Abstract
INTRODUCTION: During the 2014-2016 Ebola epidemic in West Africa and the Ebola outbreaks between 2018 and 2020 in the Democratic Republic of Congo, vaccines and other tools for prevention and treatment had to be taken through trials in exceptional circumstances using accelerated processes. MATERIALS AND METHODS: We interviewed members of ethics committees, health authorities, health professionals, and political authorities in the Democratic Republic of Congo in 2021 and held a workshop with ethics committee members and regulatory authorities from Sierra Leone and Guinea in 2022 in order to document their experiences of reviewing, approving, and regulating current and new studies during epidemics and outbreaks, and to document lessons learnt and their recommendations for the rapid review of clinical trial protocols during public health emergencies. RESULTS: Similar barriers were identified in the three countries. These were related to weak legal frameworks and partnerships between ethics committees and regulatory bodies. Inadequate human resources, outdated standard operating procedures and guidelines, and lack of finance to support timely reviews were identified. We also noted a lack of awareness from politicians, scientists, and communities about the existence and functions of regulatory bodies/ethics committees, a lack of independence, and low interest in research. Opportunities identified by the institutions in the countries concerned included training ethics committee members and networking with experienced international platforms like the African Vaccine Regulatory Forum. Laws on regulating research have been updated in Sierra Leone and in Guinea, but not yet in the Democratic Republic of Congo. CONCLUSION: Regulatory bodies have been facing many challenges in terms of a lack of a legal framework, a lack of finance, and a lack of support from politicians, scientists, and communities. Networking has been an opportunity for these regulators to mitigate these impediments.
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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 | 0.195 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".