An Investigation into the Clinical Reasoning Development of Veterinary Students
Bibliographic record
Abstract
Clinical reasoning is a fundamental skill for veterinary clinicians and a competency required of graduates by the Royal College of Veterinary Surgeons. However, it is unknown how veterinary students develop reasoning skills and where strengths and shortcomings of curricula lie. This research aimed to use the University of Nottingham School of Veterinary Medicine and Science (SVMS) as a case study to investigate the development of clinical reasoning among veterinary students. The analysis was framed in consideration of the taught, learned, and declared curricula. Sixteen staff and sixteen students from the SVMS participated separately in a total of four focus groups. In addition, five interviews were conducted with recent SVMS graduates. Audio transcriptions were used to conduct a thematic analysis. A content analysis was performed on all curriculum documentation. It was found that SVMS graduates feel they have a good level of reasoning ability, but they still experience a deficit in their reasoning capabilities when starting their first job. Overarching themes arising from the data suggest that a lack of responsibility for clinical decisions during the program and the embedded nature of the clinical reasoning skill within the curriculum could be restricting development. In addition, SVMS students would benefit from clinical reasoning training where factors influencing "real life" decisions (e.g., finances) are explored in more depth. Integrating these factors into the curriculum could lead to improved decision-making ability among SVMS graduates and better prepare students for the stressful transition to practice. These findings are likely to have implications for other veterinary curricula.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".