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Record W2592531867 · doi:10.1136/vr.103997

Evaluating veterinary practitioner perceptions of communication skills and training

2017· article· en· W2592531867 on OpenAlexaff
Michael P. McDermott, Malcolm Cobb, Victoria Tischler, Iain Robbé, Rachel Dean

Bibliographic record

VenueVeterinary Record · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsHealth Sciences CentreMemorial University of Newfoundland
FundersElanco Animal HealthColorado State University
KeywordsCommunication skillsMedical educationThematic analysisCommunication skills trainingCurriculumPerceptionSkills managementTraining (meteorology)MedicinePsychologyVeterinary medicinePedagogyQualitative research

Abstract

fetched live from OpenAlex

A survey was conducted among veterinary practitioners in the UK and the USA in 2012/2013. Thematic analysis was used to identify underlying reasons behind answers to questions about the importance of communication skills and the desire to participate in postgraduate communication skills training. Lack of training among more experienced veterinary surgeons, incomplete preparation of younger practitioners and differences in ability to communicate all contribute to gaps in communication competency. Barriers to participating in further communication training include time, cost and doubts in the ability of training to provide value. To help enhance communication ability, communication skills should be assessed in veterinary school applicants, and communication skills training should be more thoroughly integrated into veterinary curricula. Continuing education/professional development in communication should be part of all postgraduate education and should be targeted to learning style preferences and communication needs and challenges through an entire career in practice.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.580
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.537
GPT teacher head0.584
Teacher spread0.047 · 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

Citations20
Published2017
Admission routes1
Has abstractyes

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