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Record W1555358712

Assessing communication competence: a review of current tools.

2005· review· en· W1555358712 on OpenAlexaboutno aff
Julie M Schirmer, Larry Mauksch, Forrest Lang, M. Kim Marvel, Kathy Zoppi, Ronald M. Epstein, Doug Brock, Michael Pryzbylski

Bibliographic record

VenuePubMed · 2005
Typereview
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsUsabilityCompetence (human resources)Multidisciplinary approachMedical educationPsychologyCommunication skillsMedicineApplied psychologyFamily medicineComputer scienceSocial psychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The assessment of communication competence has become a major priority of medical educational, policy, and licensing organizations in the United States and Canada. Multiple tools are available to assess communication competence, but there are few studies that compare the tools. METHODS: A consensus panel of six family medicine educators evaluated 15 instruments measuring the physician-patient interview. The primary evaluation criteria came from the Kalamazoo Consensus Statement (KCS), which derived from a multidisciplinary panel of experts that defined seven essential elements of physician-patient communication. We evaluated psychometric properties of the instruments and other assessment criteria felt to be important to family physicians (exploring family issues, interview efficiency, and usability/practicality). RESULTS: Instruments that received the highest ratings on KCS elements were designed for faculty raters and varied in their practicality/usability ratings and psychometric properties. Few instruments were rated high on psychometric properties or exploring family issues. CONCLUSIONS: The process successfully reviewed and provided a framework for assessing communication skills instruments. There is a need to expand the study, including use of a larger cohort of reviewers to provide more validity to the results and minimize potential biases.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.692
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.003
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 head0.706
GPT teacher head0.562
Teacher spread0.144 · 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.

Study designNot applicable
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

Citations254
Published2005
Admission routes1
Has abstractyes

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