Development and Evaluation of vetPAL, a Student-Led, Peer-Assisted Learning Program
Bibliographic record
Abstract
Based on an idea from a final-year student, Bristol Veterinary School introduced vetPAL, a student-led, peer-assisted learning program. The program involved fifth-year (final-year) students acting as tutors and leading sessions for fourth-year students (tutees) in clinical skills and revision (review) topics. The initiative aimed to supplement student learning while also providing tutors with opportunities to further develop a range of skills. All tutors received training and the program was evaluated using questionnaires collected from tutees and tutors after each session. Tutees' self-rated confidence increased significantly in clinical skills and for revision topics. Advantages of being taught by students rather than staff included the informal atmosphere, the tutees' willingness to ask questions, and the relatability of the tutors. The small group size and the style of learning in the revision sessions (i.e., group work, discussions, and interactivity) were additional positive aspects identified by both tutees and tutors. Benefits for tutors included developing their communication and teaching skills. The training sessions were considered key in helping tutors feel prepared to lead sessions, although the most difficult aspects were the lack of teaching experience and time management. Following the successful pilot of vetPAL, plans are in place to make the program permanent and sustainable, while incorporating necessary changes based on the evaluation and the student leader's experiences running the program. A vetPAL handbook has been created to facilitate organization of the program for future years.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".