Student Experience and Clinicians’ Longitudinal Evaluations Demonstrate Diversity of Experience and Achievement of Day One Competency in a Distributed Model of Clinical Education: A Mixed Methods Study
Bibliographic record
Abstract
Numerous colleges use distributed veterinary education (DVE) to deliver most or all their students’ clinical education. This study explored students’ experiences and development of competence in a DVE program. Veterinarians evaluated 120 final-year students’ performances at the end of each 4-week clinical rotation using a four-point RIME (Reporter, Interpreter, Manager, Educator) scale. Evaluation items linked to 16 competencies, including the AVMA's Council on Education's (COE) 9 competencies and the North American Veterinary Medical Education Consortium's (NAVMEC) 7 competencies. Students were surveyed at graduation about their clinical year experience and preparedness for an expanded set of 21 competencies/subcompetencies derived from those published by the AVMA COE, NAVMEC, and the American Association of Veterinary Medical Colleges (AAVMC). Students logged 56,305 cases in ePortfolios during the year, averaging 469 cases per student. Competency scores increased during clinical year ( p < .001); scores rose most quickly in the middle third of the year. Students scored higher on some competencies than others ( p < .001), though different competencies improved at a similar rate. Seven students required remediation, which consisted of repeating one or more rotations with individualized goals and oversight; all remediated successfully. Students reported diverse spectrum of care experiences and praised the amount of hands-on experience. Students suggested additional oversight for some clinical affiliates. In conclusion, the DVE program provided a robust number and diversity of cases. Students demonstrated longitudinal gains in competency scores and reported confidence in performing competencies upon graduation. The DVE program appeared effective at meeting programmatic competency goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".