Veterinary Students' Attitudes toward the Assessment of Clinical Reasoning Using Extended Matching Questions
Bibliographic record
Abstract
For the purposes of assessment, clinical expertise has been broken down into three broad components: scientific and clinical knowledge, clinical reasoning, and practical or technical skills. This structure can be used to define the tools used for assessment of clinical students. Knowledge can be assessed through a variety of written formats, and skills through various practical assessments, including the objective structured clinical examination. The assessment of clinical reasoning is more of a challenge, and, partly in order to address this challenge, the Royal Veterinary College recently introduced veterinary clinical-scenario-based extended matching questions. A questionnaire was used to collect students' perceptions of the new format. Surprisingly, this questionnaire also delivered important insights into the students' understanding of the process of clinical reasoning itself that could be crucial in future curriculum design. Despite a theory course that introduced students to the nature of expertise and the importance of pattern recognition to experienced clinicians, some final-year students could not recognize this approach as relevant to them and objected to the way in which some of the questions were driving them to think. This may relate to the variety of methods of case management that students observe during their practical experience and the different attitudes of clinicians to the way students work up cases. Overall, the students perceived this question type as an appropriate way to test clinical reasoning and as relevant to the experience they had gained during their clinical rotations, both within the college and in veterinary practices outside it.
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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 | 0.017 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".