Development of Surgical Competence in Veterinary Students Using a Flipped Classroom Approach
Bibliographic record
Abstract
Clinical skills laboratory (CSL) training was recently introduced in the renewed veterinary curriculum at Ghent University, using models and simulators for teaching practical skills. However, time in the CSL is restricted due to the large number of students combined with limited availability of personnel. Therefore, a flipped classroom (FC) model was introduced to maximize learning experiences. The goal of the present study was to evaluate the effect of flipped classroom CSL training on students’ self-efficacy and practical surgical skills. Flipped classroom CSL training was implemented for the third-year pre-clinical students ( n = 196) in the 6-year veterinary medicine program. Prior to CSL sessions, students studied online ‘learning paths,’ including text, pictures, videos of the skills, links to background information, a forum, and a compulsory pre-class quiz. A pre- and post-test were administered before and after flipped classroom CSL training. The tests consisted of a self-efficacy scale consisting of 20 items and an objective structured clinical examination (OSCE) test of surgical skills performance. Flipped classroom CSL training resulted in significantly higher self-efficacy (score/100, pre-test 55 ± 14 vs. post-test 83 ± 8, p< .001) and surgical skills performance (score/20, pre-test 5 ± 3 vs. post-test 17 ± 3, p< .001). In conclusion, this study demonstrated the feasibility and value of implementing a flipped classroom approach in combination with CSL training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".