Researchers’ perceptions of ethical challenges in cluster randomized trials: a qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Cluster randomized trials (CRTs) pose ethical challenges for investigators and ethics committees. This study describes the views and experiences of CRT researchers with respect to: (1) ethical challenges in CRTs; (2) the ethics review process for CRTs; and (3) the need for comprehensive ethics guidelines for CRTs. METHODS: Descriptive qualitative analysis of interviews conducted with a purposive sample of 20 experienced CRT researchers. RESULTS: Informants expressed concern over the potential for bias that may result from requirements to obtain informed consent from research participants in CRTs. Informants suggested that the need for informed consent ought to be related to the type of intervention under study in a CRT. Informants rarely expressed concern regarding risks to research participants in CRTs, other than risks to privacy. Important issues identified in the research ethics literature, including fair subject selection and other justice issues, were not mentioned by informants. The ethics review process has had positive and negative impacts on CRT conduct. Informants stated that variability in ethics review between jurisdictions, and increasingly stringent ethics review in recent years, have hampered their ability to conduct CRTs. Many informants said that comprehensive ethics guidelines for CRTs would be helpful to researchers and research ethics committees. CONCLUSIONS: Informants identified key ethical challenges in the conduct of CRTs, specifically relating to identifying subjects, seeking informed consent, and the use of gatekeepers. These data have since been used to identify topics for in-depth ethical analysis and to guide the development of comprehensive ethics guidelines for CRTs.
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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 | 0.234 | 0.311 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".