Putting international research ethics guidelines to work for the benefit of developing countries.
Bibliographic record
Abstract
It has become increasingly well-recognized in recent years that an equitable distribution of the benefits of research is an important component of international research ethics. International research ethics guidelines, in particular the World Medical Association's Declaration of Helsinki, the Council for International Organizations of Medical Sciences's (CIOMS) International Ethical Guidelines for Biomedical Research Involving Human Subjects, and the United Nations Joint Programme on AIDS's (UNAIDS) Ethical Considerations in HIV Preventive Vaccine Research, have begun to assign investigators and their sponsors the task of ensuring and realizing research-related benefits for host country research subjects and their communities. These agreements impose these obligations through three primary requirements: the negotiation of agreements about the conditions under which the research will occur prior to the start of the research, the assurance of research subjects' post-trial access to effective research interventions, and the establishment of efforts to build the capacity of researchers and their institutions in host countries to participate as full partners in the research. Although the ethical need for such obligations is indisputable, these obligations present the possibility of expanding the role of investigators in a way that might be unrealistic and therefore of limited effectiveness in ensuring the fair distribution of research benefits. Despite these reasonable concerns about feasibility, investigators may be particularly well placed to play an enhanced role in maximizing the benefits of research in low- and middle-income countries (LMIC) as a result of their unique position in the global health workforce.\nIn this Commentary I argue that, under appropriate circumstances and with the appropriate training and support, investigators may be able to play a critical role in ensuring that communities in LMIC that participate in international collaborative research derive a fair share of the benefits of the research and thereby avoid being exploited. More specifically, I argue that the main activities that are required by collaborative partnership in research-engagement and negotiation-also serve as the basic means by. which a fair distribution of research benefits may be achieved. If investigators can be assured appropriate training and supportive mechanisms, and if some necessary changes in research ethics review can occur, investigators may collectively represent a potent global force for ensuring access to research benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".