Ethics Review on Externally- Sponsored Research in Developing Countries
Bibliographic record
Abstract
This chapter elaborates on some of the existing concerns and ethical issues that may arise when biomedical research protocols are proposed or funded by research institutes (private or public) in developed countries but human subjects are recruited from resource-poor countries. Over the last two decades, clinical research conducted by sponsors and researchers from developed countries to be carried out in developing countries has increased dramatically. The article examines the situations in which vulnerable populations in developing countries are likely to be exploited and/or there is no guarantee of any benefit from the research product, if proven successful, to the local community. By examining the structure and functions of ethics committees in developing countries, the article focuses on the issues which a local ethics committee should take into account when reviewing externally-sponsored research. In conclusion, by emphasizing capacity building for local research ethics committees, the article suggests that assigning the national ethics committee (if one exists) or an ethics committee specifically charged with the task of reviewing externally-sponsored proposals would bring better results in protecting human subjects as well as ensuring benefit-sharing with the local community. Request access from your librarian to read this chapter's full text.
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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 | 0.042 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".