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Record W4230729070 · doi:10.1002/0471667196.ess7005

Cluster Randomization

2005· other· en· W4230729070 on OpenAlexaff
Neil Klar, Allan Donner

Bibliographic record

VenueEncyclopedia of Statistical Sciences · 2005
Typeother
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsWestern University
Fundersnot available
KeywordsPopularityPsychological interventionCluster randomised controlled trialRandomizationCluster (spacecraft)Intervention (counseling)PsychologyMedical educationFamily medicineMedicineClinical trialComputer scienceNursingSocial psychologyPathology

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 programmes, 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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0720.002

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 head0.195
GPT teacher head0.597
Teacher spread0.401 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2005
Admission routes1
Has abstractyes

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