Getting Started with Peer-Assisted Learning in a Veterinary Curriculum
Bibliographic record
Abstract
Peer-Assisted Learning (PAL) methodologies that involve students teaching other students have been shown to be valid and effective in a variety of disciplines and are gaining increasing interest within veterinary medical education. PAL has been formally embedded within the undergraduate veterinary clinical skills curriculum at the Royal (Dick) School of Veterinary Studies (R(D)SVS), Edinburgh, since 2009 (and informally for several years before this) and has been delivered successfully to over one thousand first-year tutees by over one thousand fourth-year tutors (in their penultimate clinical year). This "teaching tip" article therefore aims to provide an informative overview of PAL for colleagues who may be interested in the methodology and to give practical tips as to how it can be successfully implemented in a veterinary degree program. We will summarize key evidence from the literature, provide a detailed example of how PAL has been implemented and optimized in our own veterinary degree program, include a subset of representative evaluation data from both tutors and tutees, and then conclude by providing colleagues with practical tips and resources (such as planning checklists and lesson plan templates) for implementing a PAL activity at their own school.
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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 | 0.011 | 0.031 |
| 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 | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".