Informed consent in cluster randomised trials: a guide for the perplexed
Bibliographic record
Abstract
In a cluster randomised trial (CRT), intact groups-such as communities, clinics or schools-are randomised to the study intervention or control conditions. The issue of informed consent in CRTs has been particularly challenging for researchers and research ethics committees. Some argue that cluster randomisation is a reason not to seek informed consent from research participants. In fact, systematic reviews have found that, relative to individually randomised trials, CRTs are associated with an increased likelihood of inadequate reporting of consent procedures and inappropriate use of waivers of consent. The objective of this paper is to clarify this confusion by providing a practical and useful framework to guide researchers and research ethics committees through consent issues in CRTs. In CRTs, it is the unit of intervention-not the unit of randomisation-that drives informed consent issues. We explicate a three-step framework for thinking through informed consent in CRTs: (1) identify research participants, (2) identify the study element(s) to which research participants are exposed, and (3) determine if a waiver of consent is appropriate for each study element. We then apply our framework to examples of CRTs of cluster-level, professional-level and individual-level interventions, and provide key lessons on informed consent for each type of CRT.
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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 | 0.534 | 0.561 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.025 | 0.041 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".