Development and Preliminary Evaluation of Student-Authored Electronic Cases
Bibliographic record
Abstract
In medical education, virtual patients are now widely used to support and enhance clinical teaching. However, there is still only a limited adoption of similar methods in veterinary education. This paper describes an initiative at the Royal Veterinary College (RVC) in London to develop student-authored cases during clinical rotations that were subsequently adapted for self-directed learning in the undergraduate curriculum as virtual patients. This approach was designed to enhance the quality of the clinical learning experience, assist in the development of clinical reasoning skills, and complement the existing teaching caseload. The creation of virtual patients involved a two-stage process. In the first stage, students compiled clinical case data and media from patients admitted to the teaching hospitals. They then used these resources to develop electronic cases using a customized Microsoft PowerPoint template that were presented at grand rounds to clinicians and other students. In the second stage, selected cases were further developed with the integration of self-assessment and additional media to create virtual patients for use in teaching. A survey was used to gather feedback on students' experiences in creating and using electronic cases. It was completed by 163 final-year students (81%) and the results indicated that all respondents had created electronic cases on one or more rotations (mean=4.3 rotations, range=1-9). Overall, the feedback suggested that the students found creating and using these cases useful and that the experience improved their approach to a case.
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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 | 0.025 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".