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Record W3034249887 · doi:10.3138/jvme.2019-0082

Collaborative Development of a Shared Framework for Competency-Based Veterinary Education

2020· article· en· W3034249887 on OpenAlexvenueno aff
Susan M. Matthew, Harold G. J. Bok, Kristin P. Chaney, Emma K. Read, Jennifer Hodgson, Bonnie R. Rush, Stephen A. May, S. Kathleen Salisbury, Jan E. Ilkiw, Jody S. Frost, Laura K. Molgaard

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)ScholarshipCurriculumMedical educationHealth careMedicineVeterinary medicinePsychologyPedagogyPolitical science

Abstract

fetched live from OpenAlex

Competency-based medical education is an educational innovation implemented in health professions worldwide as a means to ensure graduates meet patient and societal needs. The focus on student-centered education and programmatic outcomes offers a series of benefits to learners, institutions and society. However, efforts to establish a shared, comprehensive competency-based framework in veterinary education have lagged. This article reports on the development and outcome of a competency-based veterinary education (CBVE) framework created through multi-institutional collaboration with international input from veterinary educators and veterinary educational leaders. The CBVE Framework is designed to reflect the competencies expected of new graduates from member institutions of the Association of American Veterinary Medical Colleges (AAVMC). The CBVE Framework consists of nine domains of competence and 32 competencies, each supplemented with illustrative sub-competencies to guide veterinary schools in implementing competency-based education in their local context. The nine domains of competence are: clinical reasoning and decision-making; individual animal care and management; animal population care and management; public health; communication; collaboration; professionalism and professional identity; financial and practice management; and scholarship. Developed through diverse input to facilitate broad adoption, the CBVE Framework provides the foundation for competency-based curricula and outcomes assessment in veterinary education internationally. We believe that other groups seeking to design a collective product for broad adoption might find useful the methods used to develop the CBVE Framework, including establishing expertise diversity within a small-to-medium size working group, soliciting progressive input and feedback from stakeholders, and engaging in consensus building and critical reflection throughout the development process.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.112
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.112
Threshold uncertainty score0.591

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1120.066
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0080.003
Science and technology studies0.0090.013
Scholarly communication0.0160.011
Open science0.0060.025
Research integrity0.0050.011
Insufficient payload (model declined to judge)0.0050.002

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 head0.353
GPT teacher head0.551
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations62
Published2020
Admission routes1
Has abstractyes

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