Enhancing capacity of ethics review committees in developing countries: The Kenyan example
Bibliographic record
Abstract
Background. The increased number of clinical trials taking place in developing countries and the complexity of trial protocols mandate that local ethics review committees (ERCs) reviewing them have the capacity to ensure that they are conducted to the highest ethical standards.Methods. The Kenya AIDS Vaccine Initiative (KAVI) Institute of Clinical Research (ICR) (KAVI-ICR) and the Kenyan National Council for Science and Technology (NCST) embarked on an exercise to enhance the capacity of ERCs in Kenya to review such protocols. This process involved conducting an audit of all ERCs in the country, and performing training needs assessments to identify knowledge and capacity gaps. Information obtained was used to develop training materials for ERC members at workshops conducted in different parts of the country.Results. Five accredited and 13 non-accredited ERCs were identified. Four of the accredited ERCs were located in the capital city of Kenya, Nairobi. The most common challenges cited by participants during the needs assessments were excess workload, and a lack of co-ordination and/or communication between the ERCs. Subsequently, 140 ERC members from 17 institutions across the country were trained as follows: 36 from institutions in the western part of Kenya, 38 from institutions in the south-eastern coastal region, 38 from the eastern region and 44 from Nairobi.Conclusion. The KAVI-ICR and the NCST have developed training modules for training ERC members in Kenya and are in the process of developing a manual to train members. The Kenyan experience may be used to enhance the capacity of ERCs in the East African region.
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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 | 0.281 | 0.228 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.018 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".