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Record W4386327879 · doi:10.3138/jvme-2023-0022

The Importance of Adhering to Terminology When Implementing Competency-Based Veterinary Education (CBVE)

2023· article· en· W4386327879 on OpenAlexvenueno aff
Kristin P. Chaney, Jennifer Hodgson, Heidi E. Banse, Jared A. Danielson, M. Carolyn Gates, Jan E. Ilkiw, Susan M. Matthew, Emma K. Read, Shari Salisbury, Rosanne M. Taylor, Jody S. Frost

Bibliographic record

VenueJournal of Veterinary Medical Education · 2023
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsTerminologyMedical educationConsistency (knowledge bases)Veterinary educationMedicineFidelityVeterinary medicineNursingPsychologyComputer scienceCurriculumPedagogy

Abstract

fetched live from OpenAlex

The American Association of Veterinary Medical Colleges (AAVMC) Competency-Based Veterinary Education (CBVE) Model was developed with consideration of the lessons learned over the past 20 years by other health care professions regarding the implementation of outcomes-based education. As veterinary education programs consider the benefits of outcomes-based training, and more programs begin adopting this model of education, it is more critical than ever to ensure fidelity of the model for successful implementation. Fidelity, or the accuracy with which something is reproduced, was identified as an important factor in successfully implementing competency-based training in medical education (CBME). Without fidelity of the core components of CBME as defined in the medical education literature, programs were challenged to evaluate the effectiveness of the new educational model, and in some cases, this led to premature notions of failure when all components of CBME had not been successfully implemented. Consistency in terminology related to competency-based education is critical for successful implementation of the CBVE Model. The terminology used in higher education, and in other disciplines, describes concepts that are underpinned by research, just as they are in competency-based education. Without shared understanding and accurate use of terminology to describe the tools and strategies used in CBVE, there is a considerable risk of failure or even perceived failure in transitioning to CBVE. The authors of this commentary, the AAVMC Council on Outcomes-based Veterinary Education, continue to encourage veterinary programs across the world to recognize the value of the AAVMC CBVE Model in transforming veterinary education. Through use of shared terminology and consistent application of the components of the model, we envision the expansion of CBVE as an opportunity to advance veterinary education and to promote new graduate success in the veterinary profession.

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)0.118
metaresearch head score (Gemma)0.322
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.118
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.322
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0090.040
Scholarly communication0.0170.022
Open science0.0110.010
Research integrity0.0350.068
Insufficient payload (model declined to judge)0.0020.001

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 head0.065
GPT teacher head0.427
Teacher spread0.361 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

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