A Survey of Established Veterinary Clinical Skills Laboratories from Europe and North America: Present Practices and Recent Developments
Bibliographic record
Abstract
Developing competence in clinical skills is important if graduates are to provide entry-level care, but it is dependent on having had sufficient hands-on practice. Clinical skills laboratories provide opportunities for students to learn on simulators and models in a safe environment and to supplement training with animals. Interest in facilities for developing veterinary clinical skills has increased in recent years as many veterinary colleges face challenges in training their students with traditional methods alone. For the present study, we designed a survey to gather information from established veterinary clinical skills laboratories with the aim of assisting others considering opening or expanding their own facility. Data were collated from 16 veterinary colleges in North America and Europe about the uses of their laboratory, the building and associated facilities, and the staffing, budgets, equipment, and supporting learning resources. The findings indicated that having a dedicated veterinary clinical skills laboratory is a relatively new initiative and that colleges have adopted a range of approaches to implementing and running the laboratory, teaching, and assessments. Major strengths were the motivation and positive characteristics of the staff involved, providing open access and supporting self-directed learning. However, respondents widely recognized the increasing demands placed on the facility to provide more space, equipment, and staff. There is no doubt that veterinary clinical skills laboratories are on the increase and provide opportunities to enhance student learning, complement traditional training, and benefit animal welfare.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".