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Record W1914978864 · doi:10.3138/jvme.1214-124r1

Faculty Development for a New Curriculum: Implementing a Strategy for Veterinary Teachers within the Wider University Context

2015· article· en· W1914978864 on OpenAlexvenueno aff
Sheena Warman, Jane Pritchard, Sarah Baillie

Bibliographic record

VenueJournal of Veterinary Medical Education · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
FundersUniversity of Edinburgh
KeywordsCurriculumExcellenceFaculty developmentMedical educationContext (archaeology)Professional developmentCurriculum developmentQuality (philosophy)MedicinePedagogyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Faculty development in veterinary education is receiving increasing attention internationally and is considered of particular importance during periods of organizational or curricular change. This report outlines a faculty development strategy developed since October 2012 at the University of Bristol Veterinary School, in parallel with the development and implementation of a new curriculum. The aim of the strategy is to deliver accessible, contextual faculty development workshops for clinical and non-clinical staff involved in veterinary student training, thereby equipping staff with the skills and support to deliver high-quality teaching in a modern curriculum. In October 2014, these workshops became embedded within the new University of Bristol Continuing Professional Development scheme, Cultivating Research and Teaching Excellence. This scheme ensures that staff have a clear and structured route to achieving formal recognition of their teaching practice as well as access to a wide range of resources to further their overall professional development. The key challenges and constraints are discussed.

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)0.060
metaresearch head score (Gemma)0.041
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 score0.060
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0600.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0110.005
Scholarly communication0.0160.007
Open science0.0040.018
Research integrity0.0060.007
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 head0.178
GPT teacher head0.441
Teacher spread0.263 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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