The Use of Contextualized Standardized Client Simulation to Develop Clinical Reasoning in Final-Year Veterinary Students
Bibliographic record
Abstract
Clinical reasoning is an important skill for veterinary students to develop before graduation. Simulation has been studied in medical education as a method for developing clinical reasoning in students, but evidence supporting it is limited. This study involved the creation of a contextualized, standardized client simulation session that aimed to improve the clinical reasoning ability and confidence of final-year veterinary students. Sixty-eight participants completed three simulated primary-care consultations, with the client played by an actor and the pet by a healthy animal. Survey data showed that all participants felt that the session improved their clinical decision-making ability. Quantitative clinical reasoning self-assessment, performed using a validated rubric, triangulated this finding, showing an improvement in students' perception of several components of their clinical reasoning skill level from before the simulation to after it. Blinded researcher analysis of the consultation video recordings found that students showed a significant increase in ability on the history-taking and making-sense-of-data (including formation of a differential diagnosis) components of the assessment rubric. Thirty students took part in focus groups investigating their experience with the simulation. Two themes arose from thematic analysis of these data: variety of reasoning methods and "It's a different way of thinking." The latter highlights differences between the decision making students practice during their time in education and the decision making they will use once they are in practice. Our findings suggest that simulation can be used to develop clinical reasoning in veterinary students, and they demonstrate the need for further research in this area.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".