TRAINING NEEDS ASSESSMENT IN RESEARCH ETHICS EVALUATION AMONG RESEARCH ETHICS COMMITTEE MEMBERS IN THREE AFRICAN COUNTRIES: CAMEROON, MALI AND TANZANIA
Bibliographic record
Abstract
BACKGROUND: As actors with the key responsibility for the protection of human research participants, Research Ethics Committees (RECs) need to be competent and well-resourced in order to fulfil their roles. Despite recent programs designed to strengthen RECs in Africa, much more needs to be accomplished before these committees can function optimally. OBJECTIVE: To assess training needs for biomedical research ethics evaluation among targeted countries. METHODS: Members of RECs operating in three targeted African countries were surveyed between August and November 2007. Before implementing the survey, ethical approvals were obtained from RECs in Switzerland, Cameroon, Mali and Tanzania. Data were collected using a semi-structured questionnaire in English and in French. RESULTS: A total of 74 respondents participated in the study. The participation rate was 68%. Seventy one percent of respondents reported having received some training in research ethics evaluation. This training was given by national institutions (31%) and international institutions (69%). Researchers and REC members were ranked as the top target audiences to be trained. Of 32 topics, the top five training priorities were: basic ethical principles, coverage of applicable laws and regulations, how to conduct ethics review, evaluating informed consent processes and the role of the REC. CONCLUSION: Although the majority of REC members in the targeted African countries had received training in ethics, they expressed a need for additional training. The results of this survey have been used to design a training program in research ethics evaluation that meets this need.
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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 | 0.084 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".