A Novel Performance Gaps Analysis and Objective-Setting Framework for Veterinary Students in a Workplace-Based Distributed Model of Clinical Training
Bibliographic record
Abstract
This article presents the implementation of an individualised learning outcome (LO)-setting framework that is based on individual performance data at the University of Surrey School of Veterinary Medicine and showcases a best practice model for outcomes-based education. Designed to align student learning with the Royal College of Veterinary Surgeons (RCVS) Day One Competences, the framework empowers students to identify and address specific knowledge and skill gaps through a structured and data-informed process. Drawing on a range of performance indicators, including global performance assessments, structured oral examinations, skills logs, and personal reflections, the model ensures that LOs are both targeted and achievable. Unlike traditional student-led approaches, which often lack consistency and alignment with core competences, this framework introduces a more rigorous and tailored learning experience. Key to its success is the involvement of tutors who provide scaffolding and guidance to help students formulate meaningful and realistic objectives, which fosters greater engagement with clinical training and enhances readiness for practice. Although the approach offers substantial educational benefits, including improved relevance and personalisation of learning, its implementation requires careful consideration of staff workload and sustainability. To address this, strategies such as automation and virtual support are proposed. Further research is encouraged to evaluate the long-term impact of performance-led learning on student engagement during clinical rotations and subsequent graduate outcomes. Overall, this initiative represents a significant step forward in veterinary education, demonstrating how personalised, performance-informed learning can better prepare students for clinical placements and help them tailor the learning opportunities to meet their individual needs.
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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 | 0.043 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".