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Record W150633199

Communication assessment using the common ground instrument: psychometric properties.

2004· article· en· W150633199 on OpenAlexaboutno aff
Forrest Lang, Ronald McCord, Delia Anderson

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistGeneralizability theoryAccreditationCommon groundMedical educationReliability (semiconductor)Graduate medical educationInter-rater reliabilityPsychologyCommon coreApplied psychologyFamily medicineMedicineRating scaleComputer scienceCore (optical fiber)Social psychologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Recent guidelines from the Association of American Medical Colleges and from the Accreditation Council for Graduate Medical Education strongly suggest that communications teaching and assessment be part of medical education at all levels. This study's objective was to validate an instrument to assess communications skills. This instrument, Common Ground, is linked to the core, generic communication skills emphasized by the consensus statements of Toronto and Kalamazoo. METHODS: A total of 100 medical students were recruited from two medical schools and tested with four-station, communications-focused objective structured clinical examinations. Using Common Ground, trained raters performed checklist and global rating assessments. Experts globally assessed 20 representative interviews. RESULTS: Inter-rater reliability for Common Ground was 0.85 for the overall global ratings and 0.92 for the overall checklist assessment. Generalizability coefficient was 0.80 for 50 minutes of testing. The correlation between the ratings of trained raters and a panel of communication experts was 0.84. CONCLUSIONS: The Common Ground assessment instrument assesses core communication skills with sufficient reliability, validity, and generalizablity to make decisions on medical students' performance.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.092
GPT teacher head0.342
Teacher spread0.249 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations118
Published2004
Admission routes1
Has abstractyes

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