Predictors of Success in a UK Veterinary Medical Undergraduate Course
Bibliographic record
Abstract
Admission procedures for veterinary undergraduate training programs often include an interview as well as assessment of previous academic performance. In addition to pre-course factors, within-course factors such as performance in earlier years may play a role in determining success in the veterinary course. This study investigated the relationship between pre-course factors and within-course factors as predictors of success within the course. The study population consisted of six first-year cohorts, five second-year cohorts, four third-year cohorts, three fourth-year cohorts, and two fifth-year cohorts. There were a total of 1,347 students from the five-year Bachelor of Veterinary Medicine (BVetMed) program at the Royal Veterinary College (RVC). Data from these cohorts consisted of pre-entry demographic (sex, age, and nationality) and admission variables and within-course assessments. Logistic regression was used to examine the relationship between predictors and outcome. The study confirmed the value of previous academic performance in selecting students for the veterinary degree course but the value of interviews in the selection process was less clear. Within-course examination results were associated with later course outcome and high marks in continuous assessments were associated with overall success in the course. The study supports selection of students on the basis of previous academic performance but not interview scores. Continuous assessment and within-course examination results may be of value in identifying those students most likely to fail and therefore, those who need to be monitored and advised more closely.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".