MétaCan
Menu
Back to cohort

Ethical Challenges Posed by Cluster Randomization

2014· other· en· W4236546772 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2014
Typeother
Languageen
FieldComputer Science
TopicPrivacy-Preserving Technologies in Data
Canadian institutionsWestern UniversityCancer Care Ontario
Fundersnot available
KeywordsInformed consentRandomized controlled trialRandomizationInstitutional review boardContext (archaeology)Cluster (spacecraft)Clinical trialPsychologyMedicineEngineering ethicsAlternative medicineComputer sciencePsychiatrySurgeryPathologyGeographyEngineering

Abstract

fetched live from OpenAlex

Abstract The purpose of this article is to discuss the ethical challenges posed by cluster randomized trials, which is done by revisiting the development of ethical guidelines that have been largely developed in the context of individually randomized clinical trials. A recurring theme underlying the article is that the relative absence of ethical guidelines for cluster randomized trials appears to have created a research environment in which the choice of randomization unit may determine whether informed consent is deemed necessary before random assignment. It seems questionable, on both an ethical level and a methodological level, whether the unit of randomization should play such a critical role in obtaining informed consent. Editors should require all reports of randomized trials to include mention of Institutional Review Board (IRB) approval and to discuss subject consent. For cluster randomized trials, however, additional discussion of procedures used to obtain informed consent is warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Open science, Research integrity
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0200.023
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.041
GPT teacher head0.309
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it