Clinical Reasoning by Veterinary Students in the First-Opinion Setting: Is It Encouraged? Is It Practiced?
Bibliographic record
Abstract
A mixed-methods study was performed to investigate the perceived importance and efficacy of teaching clinical reasoning (CR) skills among students and faculty in a university first-opinion veterinary practice, as this has not previously been described. Qualitative analysis of interview data, discussing objectives and factors considered important for effective learning and the understanding of CR, was performed alongside quantitative analysis of the Preceptor Thinking-Promotion Scale (PTPS) and the Learner Thinking-Behavior Scale (LTBS) (assessing the level of CR encouraged by clinicians and displayed by students) in peri-consultation discussions. Themes that emerged from analysis of the interviews regarding objectives included the desire to develop data acquisition and the need to improve data manipulation and CR. Themes associated with effective learning were a positive student-centered learning environment and feedback. Type II CR was fairly well described, but recognition of the importance of type I CR was poor among clinicians and students and, in some instances, was deemed to be inappropriate. Although many clinicians and students expressed a desire to develop student CR, there was little evidence of this actually occurring in the interactions analyzed, with low PTPS and LTBS scores achieved. There was also poor understanding of whether effective teaching of CR had occurred, demonstrated by a lack of correlation between LTBS and the interaction score for development of student CR. Further training of clinicians and students of the value of type I CR in first-opinion practice is required, as well as clinician education in how best to support the development of CR in students.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".