The Importance of Student Preparedness Characteristics for Veterinary Workplace Clinical Training (WCT) in a Distributive Curriculum, from the Perspectives of Students, Academics, and Clinical Supervisors
Bibliographic record
Abstract
Veterinary students, academics, and clinical supervisors are likely to have different perspectives on what it takes to be prepared for workplace clinical training (WCT). Differing expectations could confuse students if they receive conflicting messages about the skills and attributes to which they should aspire. Furthermore, they may struggle to engage with the affordances that workplaces provide for learning if unprepared. Using a survey, we ranked 91 preparedness characteristics and seven preparedness themes for WCT for importance, according to clinical supervisors, academics, and final-year veterinary students before and after undergoing WCT in a UK veterinary school employing a distributive model of WCT. Statistical analyses were used to determine (a) rank alignment and (b) significant differences in characteristic and theme rank among groups. The correlation among characteristic rankings was strongest between students and clinical supervisors, and weakest between clinical supervisors and academics. Honesty, integrity, and dependability together formed the most important characteristic for students and clinical supervisors, whereas students' awareness that perfection is not expected was the most important characteristic for academics. The "knowledge" theme was ranked as significantly more important for academics compared with students pre-WCT. Therefore, differences in the expectations of students starting WCT have been demonstrated in this study. As the educational setting transitions from classroom to clinic, academics and students must adapt their notions of preparedness to align with conceptions of learning and teaching in the workplace, while supervisors should be mindful of students' pre-existing expectations. Continuous communication and expectation alignment are necessary for a cohesive curriculum strategy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".