Community involvement in biomedical research conducted in the global health context; what can be done to make it really matter?
Bibliographic record
Abstract
BACKGROUND: Community involvement in research has been advocated by researchers, communities, regulatory agencies, and funders with the aim of reinforcing subjects' protection and improving research efficiency. Community involvement also has the potential to improve dissemination, uptake, and implementation of research findings. The fields of community based participatory research conducted with indigenous populations and of participatory action research offer a large base of experience in community involvement in research. Rules on involving the population affected when conducting research have been established in these fields. But what is the role of community engagement in clinical research and observational studies conducted in biomedical research outside of these specific areas? More than 20 years ago, in the field of HIV medicine, regulatory bodies and funding agencies (such as the US National Institutes of Health) recommended the constitution of a formal organism, the Community Advisory Board (CAB), as part of the study requirements for HIV trials. More recently, CABs have been adopted and used in other fields of medical research, such as malaria. CABs are not without limitations, however, and there is little research on the effectiveness of their use in achieving community protection and participation. Nevertheless, CABs could be a model to import into clinical trials and observational research where no alternative model of community representation is currently being used. CONCLUSIONS: Allocating more resources to training and shifting more power to community representatives could be part of the solution to current CAB limitations. However, for researchers to be able to apply these recommendations on community involvement, certain conditions need to be met. In particular, funding agencies need to recognize the human and financial resources required for serious community involvement, and the academic environment needs to take community involvement into account when appraising, mentoring, and training researchers.
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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 | 0.432 | 0.544 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.016 | 0.072 |
| Scholarly communication | 0.036 | 0.049 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.025 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".