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Record W3025889227 · doi:10.3138/jvme.2018-0014

Pilot Study of Small Animal Rotating Intern Telephone Communication Training Using Simulated Referring Veterinarians

2020· article· en· W3025889227 on OpenAlexvenueno aff
Jordan D. Tayce, Jason B. Coe, Kate E. Creevy

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationSpecialtyMedical educationMentorshipDocumentationMedicineTraining (meteorology)Communication skillsFamily medicine

Abstract

fetched live from OpenAlex

Proficiency in client communications is now widely accepted as a significant requirement of veterinary student education, with numerous training systems in use and documentation of outcomes required for academic accreditation. Little information is available concerning communication training for veterinary house officers (interns and residents), despite the large number of new graduates who enter such programs seeking further training and mentorship. The majority of student communication training focuses on face-to-face interactions with clients and development of core communication skills. By contrast, veterinary house officers in specialty hospitals frequently communicate about cases with practitioner colleagues by telephone, to assess emergent and urgent referrals and follow up on shared cases. Successful telephone communication with these colleagues is a valuable skill to cultivate in novice interns. In this pilot study, self-reported veterinary intern confidence with communication skills improved after a telephone-based simulated referring veterinarian (RDVM) communications training experience. The use of simulated RDVMs, and telephone-based training, shows promise for incorporation into future training experiences of veterinarians at this level.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.757
GPT teacher head0.572
Teacher spread0.185 · 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 designQualitative
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

Citations1
Published2020
Admission routes1
Has abstractyes

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