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Developing New Dental Communication Skills Assessment Tools by Including Patients and Other Stakeholders

2011· article· en· W2156571783 on OpenAlexafffund
Mickey Emmons Wener, Dieter J. Schönwetter, Nita Mazurat

Bibliographic record

VenueJournal of Dental Education · 2011
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsGovernment of ManitobaUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsCommunication skillsMedical educationPatient assessmentProcess (computing)PsychologyHealth communicationNeeds assessmentHealth professionalsMedicineNursingHealth careComputer science

Abstract

fetched live from OpenAlex

Effectively using patients as teachers to provide authentic feedback is an underused strategy in dental education, but it has potential for integrating the teaching of therapeutic communication skills within the dental clinic setting. This study focuses on the absence of patient input into the design of instruments used to assess students' clinical communication skills and demonstrates how a holistic approach, with input from key stakeholders including patients, was used to produce two such instruments. The development of complementary communication assessment instruments, one for patient use and one for student use, took place in three phases. In Phase I the authors reviewed a sample of existing patient satisfaction surveys; in Phase II they captured input from stakeholders; and Phase III resulted in the generation of the patient communication assessment instrument and the student communication self-assessment instrument. This article highlights communication skill issues relevant to the education of oral health professionals and describes the rationale and process for the development of the first iteration of the patient assessment and student self-assessment clinical communication instruments.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.407
GPT teacher head0.479
Teacher spread0.072 · 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

Citations44
Published2011
Admission routes2
Has abstractyes

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