Knowledge, Opinions and Experiences of Researchers Regarding Ethical Regulation of Biomedical Research in Benin: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract Background: Ethics in biomedical research is still a fairly new concept in Africa. This work aims to assess the knowledge, attitude and experiences of Beninese researchers in regard to the national ethical regulatory framework of biomedical research in Benin.Methods: This was a cross-sectional, prospective and descriptive study, involving all the researchers fulfilling the inclusion criteria. Data were collected through a face-to-face interview using a questionnaire and analysed. Proportions and means were calculated with their confidence intervals and standard deviations, respectively.Results: Of the 110 participants included in the study, 40.9% were medical lecturers and 71.1% had been involved in more than 10 biomedical research as researcher. Less than three quarters (69.1%) were able to correctly quote the basic principles from Belmont report. The quarter (25.45%) of them knew the attributions of the National Ethics Committee for Health Research (CNERS in French) and 38.2%, the content of the legislation on health research ethics in Benin. The common ethical rules were known by 69.1% of the participants. A quarter (25.5%) of participants said they always present the study’s briefing note to their study participants and 62.7% said they systematically request informed consent. For those who do not present the briefing note to participants, the main reasons provided were the researchers' difficulties in writing the note in plain language and the participants ' limitation in understanding it.Conclusions: The foundations of a good ethical framework for health research exist in Benin. However, the deployment and use of the various legal texts deserve to be improved.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".