Ethical issues raised by cluster randomised trials conducted in low-resource settings: identifying gaps in the<i>Ottawa Statement</i>through an analysis of the PURE Malawi trial
Bibliographic record
Abstract
The increasing use of cluster randomised trials in low-resource settings raises unique ethical issues. TheOttawa Statement on the Ethical Design and Conduct of Cluster Randomised Trialsis the first international ethical guidance document specific to cluster trials, but it is unknown if it adequately addresses issues in low-resource settings. In this paper, we seek to identify any gaps in theOttawa Statementrelevant to cluster trials conducted in low-resource settings. Our method is (1) to analyse a prototypical cluster trial conducted in a low-resource setting (PURE Malawi trial) with theOttawa Statement; (2) to identify ethical issues in the design or conduct of the trial not captured adequately and (3) to make recommendations for issues needing attention in forthcoming revisions to theOttawa Statement. Our analysis identified six ethical aspects of cluster randomised trials in low-resource settings that require further guidance. The forthcoming revision of theOttawa Statementshould provide additional guidance on these issues: (1) streamlining research ethics committee review for collaborating investigators who are affiliated with other institutions; (2) the classification of lay health workers who deliver study interventions as health providers or research participants; (3) the dilemma experienced by investigators when national standards seem to prohibit waivers of consent; (4) the timing of gatekeeper engagement, particularly when researchers face funding constraints; (5) providing ancillary care in health services or implementation trials when a routine care control arm is known to fall below national standards and (6) defining vulnerable participants needing protection in low-resource settings.
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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 | 0.808 | 0.883 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| 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".