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Record W4231375187 · doi:10.1002/9780471462422.eoct049

Cluster Randomization

2006· other· en· W4231375187 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 Encyclopedia of Clinical Trials · 2006
Typeother
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWestern UniversityCancer Care Ontario
Fundersnot available
KeywordsPopularityPsychological interventionRandomizationCluster randomised controlled trialCluster (spacecraft)Intervention (counseling)PsychologyMedical educationMedicineGerontologyClinical trialFamily medicineComputer scienceNursingSocial psychology

Abstract

fetched live from OpenAlex

Abstract The purpose of this article is to present a systematic and unified treatment of comparative trials that randomize intact social units, or clusters of individuals, to different intervention groups. Such trials have become particularly widespread in the evaluation of nontherapeutic interventions, including lifestyle modification, educational programs, and innovations in the provision of health care. Their increasing popularity over the last two decades has led to an extensive body of methodology and a growing, but somewhat scattered, literature that cuts across several disciplines in the statistical, social, and medical sciences.

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)0.062
metaresearch head score (Gemma)0.153
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0620.153
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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 head0.677
GPT teacher head0.719
Teacher spread0.042 · 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