Student-Initiated Feedback Using Clinical Encounter Cards during Clinical Rotations in Veterinary Medicine: A Feasibility Study
Bibliographic record
Abstract
Despite its critical role for successful student learning, providing adequate teacher feedback is still a major issue during clinical education. In human medical education, the implementation of clinical encounter cards (CECs) has led to more frequent, timely, and structured teacher feedback. The present study aimed to introduce student-initiated CECs in a veterinary medical setting (clinical rotations). A total of 24 students were randomly assigned to a control group (standard rotations) and an intervention group where they had to ask for teacher feedback using CECs. The feasibility of implementing CECs was evaluated by examining the content of the completed CECs and by using anonymous student and teacher questionnaires. In addition, acceptance of the intervention and changes in feedback behavior were examined from both the teachers' and students' perspectives. Overall, it was shown that using CECs is not only feasible in a veterinary clinical setting but also conducive to more frequent and constructive teacher feedback. However, some teachers postponed completing the CECs due to time pressure, leading to less direct and timely feedback. Moreover, students felt more comfortable asking for feedback from less experienced, younger teachers, and teachers' quantitative ratings and open commentaries seemed to be affected by leniency bias. Finally, a focus group including teachers and students discussed these results in light of their practical experiences. This led to a streamlining of the implementation process and optimizations to facilitate future large-scale implementation. The study has implications for veterinary educators wishing to improve feedback in their institution.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".