Research ethics review during the COVID-19 pandemic: An international study
Bibliographic record
Abstract
ABSTRACT Research ethics review committees (ERCs) worldwide faced daunting challenges during the COVID-19 pandemic. There was a need to balance rapid turnaround with rigorous evaluation of high-risk research protocols in the context of considerable uncertainty. This study explored the experiences and performance of ERCs during the pandemic. We conducted an anonymous, cross-sectional, global online survey of chairs (or their delegates) of ERCs who were involved in the review of COVID-19-related research protocols after March 2020. The survey ran from October 2022 to February 2023 and consisted of 50 items, with opportunities for open text responses. Two hundred and three participants [130 from high-income countries (HICs) and 73 from low-and middle-income countries (LMICs)] completed our survey. Respondents came from diverse entities and organizations from 48 countries (19 HICs and 29 LMICs) in all World Health Organization regions. Responses show little of the increased global funding for COVID-19 research was allotted to the operation of ERCs. Few ERCs had pre-existing internal policies to address operation during public health emergencies, but almost half used existing guidelines. Most ERCs modified existing procedures or designed and implemented new ones but had not evaluated the success of these changes. Participants overwhelmingly endorsed permanently implementing several of them. Few ERCs added new members but non-member experts were consulted; quorum was generally achieved. Collaboration among ERCs was infrequent, but reviews conducted by external ERCs were recognized and validated. Review volume increased during the pandemic, with COVID-19-related studies being prioritized. Most protocol reviews were reported as taking less than three weeks. One-third of respondents reported external pressure on their ERCs from different stakeholders to approve or reject specific COVID-19-related protocols. ERC members faced significant challenges to keep their committees functioning during the pandemic. Our findings can inform ERC approaches towards future public health emergencies. To our knowledge, this is the first international, COVID-19-related study of its kind.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.149 | 0.359 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.043 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".