Cross-Sectional Survey of Student and Faculty Experiences with Feedback and Assessment of Clinical Competency on Final Year Rotations at American Association of Veterinary Medical Colleges (AAVMC) Member Institutions
Bibliographic record
Abstract
The clinical experiences, feedback, and assessment that veterinary students receive during final year rotations have a significant impact on whether they will achieve entry-level competency at the time of graduation. In this study, a cross-sectional survey was administered to American Association of Veterinary Medical Colleges (AAVMC) member institutions to collect baseline data about current feedback and assessment practices to identify key target areas for future research and educational interventions. Responses were received from 89 faculty and 155 students distributed across 25 universities. The results indicated that there are significant gaps between evidence-based approaches for delivering feedback and assessment and what was being implemented in practice. Most feedback was provided to students in the form of end-of-rotation evaluations sometimes several weeks to months after the rotation finished when faculty were unlikely to remember specific interactions with students and there were limited opportunities for students to demonstrate progress towards addressing identified concerns. Although the most valuable type of feedback identified by students was verbal feedback delivered shortly after clinical experiences, this method was often not used due to factors such as lack of time, poor learning environments within veterinary teaching hospitals, and lack of faculty training in delivering effective feedback. The results also indicated potential challenges with how non-technical domains of competence within the AAVMC Competency-Based Veterinary Education (CBVE) Framework are currently evaluated. Finding avenues to improve feedback and assessment processes in final year clinical settings is essential to ensure that veterinary students are adequately prepared for practice.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".