Success at Veterinary School: Evaluating the Influence of Intake Variables on Year-1 Examination Performance
Bibliographic record
Abstract
A major challenge in admissions to veterinary medical degrees is to select those students most suitable for clinical training programs and careers from a large pool of applicants with very high academic ability. Predicting the success of students in a veterinary course is challenging, and relatively few objective studies have been undertaken to identify factors that facilitate progression through this educational experience. Prior educational attainment is considered by some to be a good predictor of success at undergraduate level. The aims of this study were to analyze intake data such as educational history and demographic factors of students entering the University of Edinburgh and to investigate possible relationships between these data and academic performance in the first year at veterinary school. Data were collated for three veterinary intakes, including school qualification, subjects, grades, grade point average (GPA), degree classification, domicile, gender, and age. Performance was measured by marks achieved in first-year veterinary degree examinations. Relationships between marks and the influence of intake variables were statistically analyzed via ANOVA. For school-leaving entrants, the presence of straight A grades in school was linked to better exam performance. Students with an A grade in Chemistry or Biology performed better; A grades in Mathematics and Physics did not show such a consistent linkage with performance. Higher GPA was associated with better performance in first year for students in a graduate entry program. This study shows prior educational attainment does appear to be linked with subsequent performance in the first year at veterinary school.
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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".