Experiences Introducing a Team-Based Knowledge Summary to Student Veterinary Nurses/Veterinary Technicians
Bibliographic record
Abstract
It is a responsibility of veterinarians and veterinary nurse/veterinary technician practitioners to ground their decisions on sound, objective, and current evidence. Fundamental to this process is the ability to critically analyze available evidence and apply this alongside existing clinical expertise to inform clinical decision making and practice. This teaching tip describes the design and implementation of a knowledge summary and peer feedback as elements of a summative assessment of third-year veterinary nursing degree students at the Royal Veterinary College, University of London. Underlying educational theories and practical details on how to carry out the proposed innovation are discussed. Students' feedback of this assessment method was largely positive, with acknowledgment of its value in facilitating the answering of clinically relevant questions in a practical, structured, and evidence-based format that is directly transferrable to veterinary practice. For those continuing to the fourth year of the Bachelor of Science (BSc) program, it was considered good preparation for the research and literature review conducted as part of the final-year project. Feedback from faculty suggests that the assessment fulfilled its aim of ensuring improved constructive alignment and facilitating the development of higher-order cognitive skills. Others are encouraged to adopt this method of assessment to develop students' interpersonal skills, encourage their critical appraisal of evidence, and challenge traditional theories and practice.
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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 | 0.018 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".