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Record W2743218542 · doi:10.3138/jvme.0816-129r1

Evaluation of Fourth-Year Veterinary Students' Client Communication Skills: Recommendations for Scaffolded Instruction and Practice

2017· article· en· W2743218542 on OpenAlexvenueno aff
Brenda J. Stevens, April A. Kedrowicz

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsActive listeningEmpathyNonverbal communicationSession (web analytics)PsychologyCommunication skillsMedical educationCommunication skills trainingComputer scienceMedicineCommunicationSocial psychology

Abstract

fetched live from OpenAlex

Effective client communication is important for success in veterinary practice. The purpose of this project was to describe one approach to communication training and explore fourth-year veterinary students' communication skills through an evaluation of their interactions with clients during a general practice rotation. Two raters coded 20 random videotaped interactions simultaneously to assess students' communication, including their ability to initiate the session, incorporate open-ended questions, listen reflectively, express empathy, incorporate appropriate nonverbal communication, and attend to organization and sequencing. We provide baseline data that will guide future instruction in client communication. Results showed that students' communication skills require development. Half of the students sampled excelled at open-ended inquiry (n=10), and 40% (n=8) excelled at nonverbal communication. Students needed improvement on greeting clients by name and introducing themselves and their role (n=15), reflective listening (n=18), empathy (n=17), and organization and sequencing (n=18). These findings suggest that more focused instruction and practice is necessary in maintaining an organized structure, reflective listening, and empathy to create a relationship-centered approach to care.

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)0.014
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.511
GPT teacher head0.630
Teacher spread0.119 · 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 designOther design
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

Citations19
Published2017
Admission routes1
Has abstractyes

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