Initiatives to Improve Feedback Culture in the Final Year of a Veterinary Program
Bibliographic record
Abstract
Despite the recognized importance of feedback in education, student satisfaction with the feedback process in medical and veterinary programs is often disappointing. We undertook various initiatives to try to improve the feedback culture in the final clinical year of the veterinary program at the University of Bristol, focusing on formative verbal feedback. The initiatives included E-mailed guidelines to staff and students, a faculty development workshop, and a reflective portfolio task for students. Following these initiatives, staff and students were surveyed regarding their perceptions of formative feedback in clinical rotations, and focus groups were held to further explore issues. The amount of feedback appeared to have increased, along with improved recognition of feedback by students and increased staff confidence and competence in the process. Other themes that emerged included inconsistencies in feedback among staff and between rotations; difficulties with giving verbal feedback to students, particularly when it relates to professionalism; the consequences of feedback for both staff and students; changes and challenges in students' feedback-seeking behavior; and the difficulties in providing accurate, personal end-of-rotation assessments. This project has helped improve the feedback culture within our clinics; the importance of sustaining and further developing the feedback culture is discussed in this article.
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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 | 0.107 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".