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Record W2527976591 · doi:10.1177/1077558716671217

Rating Communication in GP Consultations: The Association Between Ratings Made by Patients and Trained Clinical Raters

2016· article· en· W2527976591 on OpenAlexaboutno aff
Jenni Burt, Gary Abel, Natasha Elmore, Antoinette Davey, Nadia Llanwarne, Inocencio Maramba, Charlotte Paddison, John Benson, Jonathan Silverman, Marc N. Elliott, John Campbell, Martín Roland

Bibliographic record

VenueMedical Care Research and Review · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
FundersProgramme Grants for Applied ResearchNational Institute for Health and Care Research
KeywordsCompetence (human resources)MedicineFamily medicineCommunication skillsAssociation (psychology)MEDLINEPsychologyMedical educationSocial psychology

Abstract

fetched live from OpenAlex

Patient evaluations of physician communication are widely used, but we know little about how these relate to professionally agreed norms of communication quality. We report an investigation into the association between patient assessments of communication quality and an observer-rated measure of communication competence. Consent was obtained to video record consultations with Family Practitioners in England, following which patients rated the physician's communication skills. A sample of consultation videos was subsequently evaluated by trained clinical raters using an instrument derived from the Calgary-Cambridge guide to the medical interview. Consultations scored highly for communication by clinical raters were also scored highly by patients. However, when clinical raters judged communication to be of lower quality, patient scores ranged from "poor" to "very good." Some patients may be inhibited from rating poor communication negatively. Patient evaluations can be useful for measuring relative performance of physicians' communication skills, but absolute scores should be interpreted with caution.

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)0.009
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.433
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.344
GPT teacher head0.564
Teacher spread0.220 · 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 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

Citations26
Published2016
Admission routes1
Has abstractyes

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