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Record W1998167404 · doi:10.1136/bmj.328.7450.1240

Review of instruments for peer assessment of physicians

2004· review· en· W1998167404 on OpenAlexaboutno aff
Richard G. Evans, Glyn Elwyn, Adrian Edwards

Bibliographic record

VenueBMJ · 2004
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsSnowball samplingPsychologyReliability (semiconductor)Inclusion (mineral)Construct validityConstruct (python library)Rating scaleValidityMedical educationMEDLINEApplied psychologyPsychometricsMedicineClinical psychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To identify existing instruments for rating peers (professional colleagues) in medical practice and to evaluate them in terms of how they have been developed, their validity and reliability, and their appropriateness for use in clinical settings, including primary care. DESIGN: Systematic literature review. DATA SOURCES: Electronic search techniques, snowball sampling, and correspondence with specialists. STUDY SELECTION: The peer assessment instruments identified were evaluated in terms of how they were developed and to what extent, if relevant, their psychometric properties had been determined. RESULTS: A search of six electronic databases identified 4566 possible articles. After appraisal of the abstracts and in depth assessment of 42 articles, three rating scales fulfilled the inclusion criteria and were fully appraised. The three instruments did not meet established standards of instrument development, as no reference was made to a theoretical framework and the published psychometric data omitted essential work on construct and criterion validity. Rater training was absent, and guidance consisted of short written instructions. Two instruments were developed for a hospital setting in the United States and one for a primary care setting in Canada. CONCLUSIONS: The instruments developed to date for physicians to evaluate characteristics of colleagues need further assessment of validity before their widespread use is merited.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.565
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.520
Teacher spread0.435 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations127
Published2004
Admission routes1
Has abstractyes

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