Ethics committees for biomedical research in some African emerging countries: which establishment for which independence? A comparison with the USA and Canada
Bibliographic record
Abstract
CONTEXT: The conduct of medical research led by Northern countries in developing countries raises ethical questions. The assessment of research protocols has to be twofold, with a first reading in the country of origin and a second one in the country where the research takes place. This reading should benefit from an independent local ethical review of protocols. Consequently, ethics committees for medical research are evolving in Africa. OBJECTIVE: To investigate the process of establishing ethics committees and their independence. METHOD: Descriptive study of 25 African countries and two North American countries. Data were recorded by questionnaire and interviews. Two visits of ethics committee meetings were conducted on the ground: over a period of 3 months in Kigali (Rwanda) and 2 months in Washington DC (USA). RESULTS: 22 countries participated in this study, 20 from Africa and two from North America. The response rate was 80%. 75% of local African committees developed into national ethics committees. During the last 5 years, these national committees have grown on a structural level. The circumstances of creation and the general context of underdevelopment remain the major challenges in Africa. Their independence could not be ensured without continuous training and efficient funding mechanisms. Institutional ethics committees are well established in USA and in Canada, whereas ethics committees in North America are weakened by the institutional affiliation of their members. CONCLUSION: The process of establishing ethics committees could affect their functioning and compromise their independence in some African countries and in North America.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".