Ethical frameworks in clinical research processes during COVID-19: a scoping review
Bibliographic record
Abstract
OBJECTIVES: In response to the COVID-19 pandemic there have been significant developments in research, its conduct and the supporting ethical framework. While many protocols have been delayed, halted or modified, other research efforts have been accelerated, generating controversy. The goal of this paper is to determine the rates of references surrounding the ethical oversight of research as reported in current COVID-19-related research publications. DESIGN: Scoping review. SETTING: Population-based observational or interventional studies from December 2019 to May 2020 with sample size of two or more. Studies were searched through electronic databases including Medline, EMBASE, and Cochrane CENTRAL Register of Controlled Trials. PARTICIPANTS: Eligibility criteria included participants within published studies who tested positive for COVID-19. MAIN OUTCOMES AND MEASURES: Data were extracted and charting methods included taking note of references to ethical frameworks, institutional review board (IRB), ethics committee (EC) or research ethics board (REB) involvement, consent processes, and other variables. RESULTS: 11 556 articles were screened, with 656 included in the final analysis. References to ethics were present in 530 (80.8%) studies, with 491 (74.8%) involving IRB/ECs/REBs and 126 (19.2%) not referencing ethics. Consent processes were outlined in 201 (30.6%) studies, with 198 (30.2%) reporting that they obtained consent waivers, however, 257 (39.2%) did not mention consent at all. Differences (p<0.001) in ethics-related references were apparent when analysed by continent, publication type, sample size and IF. CONCLUSIONS: The majority of published articles pertaining to COVID-19 research made mention of ethical considerations, however, national and regional variations in research ethics review requirements introduce heterogeneity between studies and raise important questions about the conduct of scientific research during global public emergencies. TRIAL REGISTRATION NUMBER: Open Science Framework: https://osfio/z67wb.
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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 | 0.294 | 0.554 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".