The Challenge of Timely, Responsive and Rigorous Ethics Review of Disaster Research: Views of Research Ethics Committee Members
Bibliographic record
Abstract
BACKGROUND: Research conducted following natural disasters such as earthquakes, floods or hurricanes is crucial for improving relief interventions. Such research, however, poses ethical, methodological and logistical challenges for researchers. Oversight of disaster research also poses challenges for research ethics committees (RECs), in part due to the rapid turnaround needed to initiate research after a disaster. Currently, there is limited knowledge available about how RECs respond to and appraise disaster research. To address this knowledge gap, we investigated the experiences of REC members who had reviewed disaster research conducted in low- or middle-income countries. METHODS: We used interpretive description methodology and conducted in-depth interviews with 15 respondents. Respondents were chairs, members, advisors, or coordinators from 13 RECs, including RECs affiliated with universities, governments, international organizations, a for-profit REC, and an ad hoc committee established during a disaster. Interviews were analyzed inductively using constant comparative techniques. RESULTS: Through this process, three elements were identified as characterizing effective and high-quality review: timeliness, responsiveness and rigorousness. To ensure timeliness, many RECs rely on adaptations of review procedures for urgent protocols. Respondents emphasized that responsive review requires awareness of and sensitivity to the particularities of disaster settings and disaster research. Rigorous review was linked with providing careful assessment of ethical considerations related to the research, as well as ensuring independence of the review process. CONCLUSION: Both the frequency of disasters and the conduct of disaster research are on the rise. Ensuring effective and high quality review of disaster research is crucial, yet challenges, including time pressures for urgent protocols, exist for achieving this goal. Adapting standard REC procedures may be necessary. However, steps should be taken to ensure that ethics review of disaster research remains diligent and thorough.
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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 | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.663 | 0.704 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.015 | 0.033 |
| Insufficient payload (model declined to judge) | 0.002 | 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".