A Novel Performance Gaps Analysis and Objective-Setting Framework for Veterinary Students in a Workplace-Based Distributed Model of Clinical Training

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.