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Record W2347066482 · doi:10.3138/jvme.0815-144r

Utility of an Equine Clinical Skills Course: A Pilot Study

2016· article· en· W2347066482 on OpenAlexvenueno aff
Bruce W. Christensen, Jared A. Danielson

Bibliographic record

VenueJournal of Veterinary Medical Education · 2016
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)Medical educationGeneralizability theoryCourse (navigation)Skills managementCourse evaluationPsychologyMedicineHigher education

Abstract

fetched live from OpenAlex

Recent publications have revealed inadequacies in the veterinary training of future equine practitioners. To help address this problem, a 2-week Equine Clinical Skills course was designed and implemented to provide fourth-year veterinary students with opportunities to have hands-on experience with common equine clinical skills using live animals and cadavers. Alumni and employers of alumni were surveyed to determine whether or not students participating in the course were more competent performing clinical skills during their first year post-graduation than those who had not participated in the course. Students who participated in the course were also surveyed before and after completing the course to determine whether or not their self-assessed skills improved during the course. Alumni who had taken the course rated their ability to perform the clinical skills more highly than alumni who had not taken the course. Similarly, students participating in the course indicated that they were significantly more able to perform the clinical skills after the course than when it began. Employers did not indicate a difference between the clinical skills of those who had taken the course and those who had not. Because this study involved a limited number of respondents from one institution, further studies should be conducted to replicate these findings and determine their generalizability.

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)0.007
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.604
GPT teacher head0.655
Teacher spread0.051 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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