Evaluating the Experiences of Novice Veterinary Clinical Practice Educators: A Qualitative Reflection on a UK Training Program
Bibliographic record
Abstract
Within the evolving landscape of veterinary education in the United Kingdom, an increasing shift toward a distributed model of instruction necessitates that clinicians who assume the role of novice educators, receive training as clinical educators. The University of Surrey has pioneered a training program aimed at promoting understanding and application of educational theory in veterinary educator identity development. This study investigated the reflections of novice educators upon conclusion of their training to delineate the program's efficacy and identify areas of educator training necessitating further research and enhancement. A convenience sample of 53 reflective narratives was subjected to inductive thematic analysis. Three principal themes emerged. The first encapsulated an enhanced cognizance of student learning needs, underscored by foundational learning theories. Notably, the introduction of "feedforward" and the incorporation of student reflection within the feedback mechanism were identified as innovative concepts. The second theme revolved around the personal growth experienced because of participation in the training program, with 47% of reflections articulating profound introspection. The final theme explored the perceptions of the rewards and challenges associated with balancing the educational program with routine professional responsibilities, highlighting an increase in self-confidence and the obstacles encountered in allocating time for training.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".