Engaging research ethics committees to develop an ethics and governance framework for best practices in genomic research and biobanking in Africa: the H3Africa model
Bibliographic record
Abstract
In the past decade, there has been an increase in genomic research and biobanking activities in Africa. Research initiatives such as the Human Heredity and Health in Africa (H3Africa) Consortium are contributing to the development of scientific capacity and infrastructure to support these studies on the continent. Despite this growth, genomic research and biobanking have raised important ethical challenges for key research stakeholders, including members of research ethics committees. One of these is the limited ethical and regulatory frameworks to guide the review and conduct of genomic studies, particularly in Africa. This paper is a reflection on a series of consultative activities with research ethics committees in Africa which informed the development of an ethics and governance framework for best practices in genomic research and biobanking in Africa. The paper highlights the engagement process and the lessoned learned.
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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 | 0.382 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.062 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.021 | 0.027 |
| 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".