Research Involving Humans in African Countries: A Case for Domestic Legal Frameworks
Bibliographic record
Abstract
In recent years, there has been considerable discussion of the ethical challenges of conducting research in developing countries, including African countries. Although few empirical studies have been conducted in relation to this, some commentators have cited the lax regulatory environment in many developing countries, including African countries, as one of the motivating factors for increased research by pharmaceutical companies and other developed countries' sponsors. With the recent increase in research in the developing world including African countries have come allegations of unethical conduct which has exposed research participants to harm. Certain allegations of unethical conduct underscore the need for regulation of research in African countries. Chief among the issues raised by these allegations are the failure to obtain informed consent and ethics approval for studies conducted on humans. The Pfizer incident in Nigeria illustrates these issues. African countries are, however, increasingly beginning to grapple with issues of the ethical conduct of research. Some have recently developed national guidelines and established national ethics review committees. But, at present, many African countries lack legislative frameworks governing research involving humans, including biomedical research. This paper contends that given the need to protect the safety, dignity and welfare of persons involved in research in developing countries, ensuring ethical conduct of research in African countries should involve the use of functional and comprehensive legal frameworks. Comprehensive domestic legal frameworks, which include legislation, it is argued, can play an important role in clarifying the requirements for the ethical conduct of research, outlining responsibilities and providing a system of accountability.
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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 | 0.214 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.080 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.025 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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".