Bibliographic record
Abstract
Assessment is an important aspect of veterinary education from the point of view of setting standards, driving learning, providing feedback, and reassuring society that veterinarians are competent to assume the responsibilities entrusted to them. However, no single format exists that can, by itself, assess the complex mixture of knowledge and skills essential to the veterinarian's role. The areas that are most challenging to assess are those involving behaviors and attitudes. These include the various technical skills required for diagnosis and treatment. One approach, aimed at retaining validity but improving reliability compared with traditional, more subjective methods, first described in medicine 35 years ago, is the Objective Structured Clinical Examination (OSCE), which has been introduced into veterinary education as the Objective Structured Practical Veterinary Examination (OSPVE) and run at the Royal Veterinary College since 2004. This approach is good for the assessment of competence in relation to isolated techniques and whole procedures but has been criticized for the way in which these are tested out of context. However, further development of structured clinical assessments, such as the mini-Clinical Examination and the Direct Observation of Procedural Skills, may help address some of these limitations, and the use of multi-source feedback, particularly client feedback, may allow the further domains of professional behaviors, attitudes, and communication to be judged and developed.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".