Implementation of a Blended Learning Module to Teach Handling, Restraint, and Physical Examination of Cats in Undergraduate Veterinary Training
Bibliographic record
Abstract
Cats can be easily stressed in a clinical (training) setting and may show unpredictable reactions and patterns of defensive aggression. This can be a complicating factor in undergraduate veterinary training. Inexperienced veterinary students can evoke defensive feline behavior that negatively affects learning outcomes and animal welfare. As a result, restraint techniques and physical examination of cats was hardly practiced in pre-clinical training at Utrecht University. To overcome this, a new blended learning module was developed using a lecture on feline behavior; e-learning modules about feline behavior, handling, restraint, and physical examination skills; and redesigned practical sessions in which live animals and manikins were used. The aim of this study was to investigate how students' perceptions of competence and confidence changed regarding feline behavior, handling, restraint, and physical examination skills after the new module was implemented. Questionnaires were used for quantitative analysis, and focus groups were used for qualitative analysis. The results show that compared with students who followed the standard module, students who participated in the blended learning module scored higher in feeling confident with handling animals, feeling competent to perform physical examination on cats, and ability to assess whether a cat is stressed. Students with less experience with cats were more likely to show improvement in assessing a cat's stress level than students who had much experience with cats. The results demonstrate that the blended learning module improves students' learning outcomes regarding feline skills training and adds to reduction, refinement, and replacement of the use of live cats.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".