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Record W2751614104 · doi:10.3138/jvme.0816-137r1

Improving Student Engagement in Veterinary Business Studies

2017· article· en· W2751614104 on OpenAlexvenueno aff
Elizabeth Armitage‐Chan, Elizabeth Jackson

Bibliographic record

VenueJournal of Veterinary Medical Education · 2017
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumFormative assessmentThematic analysisStudent engagementContext (archaeology)Medical educationPsychologyPerceptionPedagogyQualitative researchMedicineSociology

Abstract

fetched live from OpenAlex

Improving Student Engagement in Veterinary Business StudiesIn a densely packed veterinary curriculum, students may find it particularly challenging to engage in the less overtly clinical subjects, yet pressure from industry and an increasingly competitive employment market necessitate improved veterinary student education in business and management skills. We describe a curriculum intervention (formative reflective assignment) that optimizes workplace learning opportunities and aims to provide better student scaffolding for their in-context business learning. Students were asked to analyze a business practice they experienced during a period of extra-mural studies (external work placement). Following return to the college, they were then instructed to discuss their findings in their study group, and produce a group reflection on their learning. To better understand student engagement in this area, we analyzed individual and group components of the assignment. Thematic analysis revealed evidence of various depths of student engagement, and provided indications of the behaviors they used when engaging at different levels. Interactive and social practices (discussing business strategies with veterinary employees and student peers) appeared to facilitate student engagement, assist the perception of relevance of these skills, and encourage integration with other curriculum elements such as communication skills and clinical problem solving.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.569
GPT teacher head0.621
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

Citations4
Published2017
Admission routes1
Has abstractyes

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