Behind the scenes of research ethics committee oversight: a qualitative research study with committee chairs in the Middle East and North Africa region
Bibliographic record
Abstract
BACKGROUND: Research cites shortcomings and challenges facing research ethics committees in many regions across the world including Arab countries. This paper presents findings from qualitative in-depth interviews with research ethics committee (REC) chairs to explore their views on the challenges they face in their work with the oversight of research involving human populations. METHODS: Virtual in-depth interviews were conducted with chairs (n = 11) from both biomedical and/or social-behavioral research ethics committees in six countries, transcribed, coded and subject to thematic analysis for recurring themes. RESULTS: Two sets of recurring themes impede the work of the committees and pose concerns for the quality of the research applications: (1) procedures and committee level challenges such as heavy workload, variations in member qualification, impeding bureaucratic procedures, member overwork, and intersecting socio-cultural values in the review process; (2) inconsistencies in the researchers' competence in both applied research ethics and research methodology as revealed by their applications. CONCLUSIONS: Narratives of REC chairs are important to shed light on experiences and issues that are not captured in surveys, adding to the body of knowledge with implications for the region, and low- and middle-income countries (LMICs) in other parts of the world. International research collaborations could benefit from the findings.
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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 | 0.272 | 0.455 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.063 |
| 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".