Improving Student Engagement in Veterinary Business Studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".