Ethical and policy issues in cluster randomized trials: rationale and design of a mixed methods research study.
Bibliographic record
Abstract
BACKGROUND: Cluster randomized trials are an increasingly important methodological tool in health research. In cluster randomized trials, intact social units or groups of individuals, such as medical practices, schools, or entire communities--rather than individual themselves--are randomly allocated to intervention or control conditions, while outcomes are then observed on individual cluster members. The substantial methodological differences between cluster randomized trials and conventional randomized trials pose serious challenges to the current conceptual framework for research ethics. The ethical implications of randomizing groups rather than individuals are not addressed in current research ethics guidelines, nor have they even been thoroughly explored. The main objectives of this research are to: (1) identify ethical issues arising in cluster trials and learn how they are currently being addressed; (2) understand how ethics reviews of cluster trials are carried out in different countries (Canada, the USA and the UK); (3) elicit the views and experiences of trial participants and cluster representatives; (4) develop well-grounded guidelines for the ethical conduct and review of cluster trials by conducting an extensive ethical analysis and organizing a consensus process; (5) disseminate the guidelines to researchers, research ethics boards (REBs), journal editors, and research funders. METHODS: We will use a mixed-methods (qualitative and quantitative) approach incorporating both empirical and conceptual work. Empirical work will include a systematic review of a random sample of published trials, a survey and in-depth interviews with trialists, a survey of REBs, and in-depth interviews and focus group discussions with trial participants and gatekeepers. The empirical work will inform the concurrent ethical analysis which will lead to a guidance document laying out principles, policy options, and rationale for proposed guidelines. An Expert Panel of researchers, ethicists, health lawyers, consumer advocates, REB members, and representatives from low-middle income countries will be appointed. A consensus conference will be convened and draft guidelines will be generated by the Panel; an e-consultation phase will then be launched to invite comments from the broader community of researchers, policy-makers, and the public before a final set of guidelines is generated by the Panel and widely disseminated by the research team.
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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 | 0.580 | 0.593 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.016 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".