Student Preparation and the Power of Visual Input in Veterinary Surgical Education: An Empirical Study
Bibliographic record
Abstract
In recent years, veterinary educational institutions have implemented alternative teaching methods, including video demonstrations of surgical procedures. However, the power of the dynamic visual input from videos in relation to recollection of a surgical procedure has never been evaluated. The aim of this study was to investigate how veterinary surgical students perceived the influence of different educational materials on recollection of a surgical procedure. Furthermore, we investigated if surgical technique was associated with a certain method of recollection or use of educational material. During a basic surgical skills course, 112 fourth-year veterinary students participated in the study by completing a questionnaire regarding method of recollection, influence of individual types of educational input, and homework preparation. Furthermore, we observed students performing an orchiectomy in a terminal pig lab. Preparation for the pig lab consisted of homework (textbook, online material, including videos), lecture, cadaver lab, and toy animal models in a skills lab. In the instructional video, a detail was used that was not described elsewhere. Results show that 60% of the students used a visual dynamic method as their main method of recollection and that video was considered the most influential educational input with respect to recollection of a specific procedure. Observation of students' performance during the orchiectomy showed no clear association with students' method of recollection but a significant association (p=.002) with educational input. Our results illustrate the power of a visual input and support prior findings that knowledge is constructed from multiple sources of information.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".