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Record W2905564040 · doi:10.3138/jvme.0617-073r

The Development of Entrustable Professional Activities for Competency-Based Veterinary Education in Farm Animal Health

2018· article· en· W2905564040 on OpenAlexvenueno aff
Chantal C. M. A. Duijn, Olle ten Cate, W.D.J. Kremer, Harold G. J. Bok

Bibliographic record

VenueJournal of Veterinary Medical Education · 2018
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumDelphi methodMedical educationMedicineCompetence (human resources)Veterinary medicineCore competencyConstruct (python library)DelphiAnimal healthNursingPsychologyComputer sciencePedagogy

Abstract

fetched live from OpenAlex

Entrustable professional activities (EPAs) are professional tasks that can be entrusted to a student under a given level of supervision once he or she has demonstrated competence in these tasks. The EPA construct was conceived to increase transparency in objectives for clinical workplace learning and to help ensure patient safety and the quality of care. A first step in implementing EPAs in a veterinary curriculum is to identify the core EPAs of the profession. The aim of this study was to develop EPAs for farm animal health. An initial set of 36 EPAs for farm animal health was prepared by a team of six veterinarians and curriculum developers and used in a modified Delphi study. In this iterative process, the EPAs were evaluated until higher than 80% agreement was reached. Of 83 veterinarians who participated, 39 (47%) completed the Delphi procedure. After two rounds, the panel reached consensus. A small expert group further refined and reorganized the EPAs for educational purposes into seven core EPAs for farm animal health and 29 sub-EPAs. This study is an important step in optimizing competency-based training in veterinary medicine. Future steps are to implement EPAs in the curriculum and train supervisors to assess students' ability to perform EPAs with increasing levels of independence.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.442
Teacher spread0.388 · 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 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

Citations39
Published2018
Admission routes1
Has abstractyes

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