Ethical issues in the design and conduct of stepped-wedge cluster randomized trials in low-resource settings
Bibliographic record
Abstract
Abstract Background Stepped-wedge cluster randomized trials (SW-CRTs) are increasingly popular in health-related research in both high- and low-resource settings. There may be specific ethical issues that researchers face when designing and conducting SW-CRTs in low-resource settings. Knowledge of these issues can help to improve the ethical conduct of SW-CRTs in a global health context. Methods We performed an ethical analysis of two studies using SW-CRT designs in low-resource settings: the Que Vivan Las Madres study conducted from 2014 to 2017 in Guatemala and the Atmiyata study conducted from 2017 to 2018 in rural parts of India. For both case studies, we identified and evaluated the classification of the study as research or nonresearch and the ethical issues regarding the justification of the design, including the delayed rollout of an intervention that had a promising effect. Results In our case studies, some minor ethical issues surfaced about the registration and stakeholder pressure on the order of randomization, but both included good justification for the design and delayed rollout. Our analysis did, however, demonstrate that careful consideration of the role of randomization and registration of the trials is important. Discussion SW-CRTs can provide an opportunity for rigorous evaluation of interventions destined to be rolled out on the basis of limited evidence. Furthermore, in SW-CRTs, the underlying objective is often to provide a robust evaluation of the effectiveness for generalized dissemination, and this makes the SW-CRT no less a research study than any other form of cluster randomized trial. Conclusion The design and conduct of stepped-wedge cluster randomized trials raises at least two ethical issues that need special consideration in both high- and low-resource settings: the justification for using the design, specifically the delayed rollout of the intervention to the control group, and the classification of the study as research or nonresearch. In our case studies, these issues did not seem to raise special ethical scrutiny in low-resource settings. Further ethical evaluation will hopefully result in specific ethical guidelines for the use of SW-CRTs in both high- and low-resource settings to contribute to responsible functioning of these trials and adequate protection of participants.
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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 | 0.727 | 0.800 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".