Teaching and Assessment of Clinical Reasoning Skills in a Case-Based Veterinary Cardiology Elective
Bibliographic record
Abstract
Clinical reasoning (CR) is an important clinical competency for effective veterinary practice. We hypothesized that implementing an explicit 7-week CR curriculum taught in a large-enrollment elective veterinary cardiology course would improve students' awareness of clinical reasoning principles, self-efficacy of CR skills, and application of CR principles in clinical case analyses. A secondary aim was to assess the impact of peer review as a means of providing feedback in a large classroom setting. A mixed method approach was used with veterinary students ( N = 78) in a cardiology elective course meeting twice weekly for a half-semester (7 weeks). Course content included a 1-week introduction to CR led by the instructor and 6 weeks of instructor-facilitated, case-based learning. Quantitative and qualitative data were collected, including pre- and post-course surveys, weekly peer reviews for six clinical case assignments, and instructor-graded clinical cases for three case assignments. Students reported improved self-efficacy across all CR skill categories ( p < .001) and significant improvement in applied CR skills was demonstrated in both peer- ( p < .001) and instructor-graded assignments ( p < .001). Peer reviews provided a means for students to reflect on and internalize CR skills, which may play a role in improved self-efficacy. In an elective cardiology course, implementing an explicit CR curriculum resulted in improved student awareness and self-efficacy of CR, as well as improved applied CR skills.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".