Bibliographic record
Abstract
BACKGROUND: Medical ethics has existed since the time of Hippocrates. However, formal training in bioethics did not become established until a few decades ago. Bioethics has gained a strong foothold in health sciences in the developed world, especially in Europe and North America. The situation is quite different in many developing countries. In most African countries, bioethics - as established and practiced today in the west- is either non-existent or is rudimentary. DISCUSSION: Though bioethics has come of age in the developed and some developing countries, it is still largely "foreign" to most African countries. In some parts of Africa, some bioethics conferences have been held in the past decade to create research ethics awareness and ensure conformity to international guidelines for research with human participants. This idea has arisen in recognition of the genuine need to develop capacity for reviewing the ethics of research in Africa. It is also a condition required by external sponsors of collaborative research in Africa. The awareness and interest that these conferences have aroused need to be further strengthened and extended beyond research ethics to clinical practice. By and large, bioethics education in schools that train doctors and other health care providers is the hook that anchors both research ethics and clinical ethics. SUMMARY: This communication reviews the current situation of bioethics in Africa as it applies to research ethics workshops and proposes that in spite of the present efforts to integrate ethics into biomedical research in Africa, much still needs to be done to accomplish this. A more comprehensive approach to bioethics with an all-inclusive benefit is to incorporate formal ethics education into health training institutions in Africa.
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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 | 0.075 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".