The Challenges and Issues of Undergraduate Student Retention and Attainment in UK Veterinary Medical Education
Bibliographic record
Abstract
Student retention and attainment has recently been identified as a key area for development in veterinary medical education enquiry. Woodfield's research on retention and attainment across the UK disciplines has yielded some unique information about the challenges and issues of students who study veterinary medicine and related subjects. The present literature review aims to expand on Woodfield's findings and explain important issues about retention and attainment across veterinary medicine. Overall, the subject of retention and attainment in undergraduate veterinary medical education needs a great deal more empirical attention, such as data on the retention and attainment of mature and widening access students, and the effects of students being placed at remote locations during their studies. Our findings also cover some unsurprising issues: the dominance of women in a profession that is principally lead by men, the underrepresentation of black and minority ethnic (BME) students in veterinary medicine, and the effects of content overload in the veterinary medical curriculum. Based on data gathered by Woodfield and our investigation of the scholarly and gray literatures, we offer an overview of gaps in current knowledge and recommendations for further research.
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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 | 0.025 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".