Reflections of Novice Veterinary Clinical Educators on Feedback Training: Insights from a UK Training Programme
Bibliographic record
Abstract
Abstract Within veterinary education, there is an increasing shift toward a distributed teaching model, requiring clinicians to assume roles as novice educators. To support their development, the University of Surrey pioneered a training program focused on promoting educational theory and feedback delivery skills. This study investigates the reflections of 79 novice clinical educators on their experiences with structured feedback training, analyzed using inductive thematic analysis. Five key themes emerged: adopting a structured feedback approach, fostering self-assessment and reflection, providing specific and constructive feedback, creating a supportive learning environment, and overcoming challenges in delivering negative feedback. Findings revealed that 99% ( n = 79) of educators recognised the importance of structured feedback, advocating for established models to guide delivery. Additionally, 87% ( n = 69) highlighted the value of self-reflection, viewing feedback as a two-way dialogue. Specific and constructive feedback was deemed critical by 76% ( n = 60), emphasizing the balance between positive reinforcement and areas for improvement. Creating a supportive learning environment was seen as essential by 66% ( n = 52) of educators, while 37% ( n = 29) acknowledged challenges in delivering negative feedback due to concerns about student demotivation. Training helped reframe negative feedback as a growth opportunity, promoting actionable and constructive guidance. The study suggests redefining “feedback sessions” as “reflective teaching sessions” to better capture the interactive and developmental nature of the process. These findings underscore the necessity of structured training for novice clinical educators, advocating for clear frameworks, reflective dialogue, and a reframed approach to feedback delivery to enhance student learning.
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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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".