Exploring Student Experiences of an Undergraduate Certificate in Veterinary Medical Education
Bibliographic record
Abstract
The ability to teach is recognized as a core skill for many professionals, including veterinarians, but undergraduate opportunities to develop this skill are not always available. A complementary teaching certificate offered during the clinical years of an undergraduate veterinary program was evaluated to investigate student experiences of the program and the perceived benefits and challenges of participating. The study used a mixed methods approach with questionnaires to provide an overview of the participant experience and semi-structured interviews to gain a deeper insight into students' experiences of the program. Two cohorts completed questionnaires comprising Likert-style and open-ended questions on the 3-year teaching certificate, the first cohort after 1 year of the program and the second cohort at completion. Interviews with participants from both cohorts were thematically analyzed to identify recurring themes. An average of 27% of students per academic year enrolled in the certificate program, most of whom completed it. Additionally, four to six students per cohort applied for Associate Fellow of the Higher Education Academy (AFHEA), and 19 students have achieved this recognition. Key themes from the data included that students felt the certificate built their confidence, increased their veterinary knowledge, and helped them become better teachers, with time management and reflection the biggest challenges. The Undergraduate Certificate of Veterinary Medical Education was seen as a good teaching foundation, while working toward the AFHEA provided some insight into higher education and academic careers. A structured teaching program offers students the opportunity to develop their learning and reflection both as students and future educators.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".