Using Creativity as an Educational Tool in Veterinary Surgery: Students’ Perceptions and Surgical Performance
Bibliographic record
Abstract
With the aim of improving students' ability to handle the complexity of surgery, we introduced a creative assignment in a veterinary surgical course. We hypothesized that by using this active, inductive educational method, reflection, creativity and self-efficacy in student novice surgeons could be improved. During a companion animal surgical course an intervention group was investigated against a control group. Twenty-nine fourth-year students were instructed in ovariohysterectomy by classical lectures, while 23 fourth-year students were provided with creative materials and assigned to consider and illustrate how to perform the procedure themselves. Surgical performance was assessed for both groups using a modified Objective Structured Assessment of Technical Skills (OSATS) while performing a simulated ovariohysterectomy. Furthermore, both groups were investigated with respect to how they would handle a specific hypothetical surgical complication. Semi-structured interviews were conducted with 17 intervention-group students and were analyzed using thematic analysis. The intervention group showed a significantly better performance and needed significantly less help with the surgical complication than the control group students. Data from interviews furthermore demonstrated that students believed the creative intervention produced increased reflection, more creative initiatives, and a feeling of security before surgery. Our study results thus indicate that an educational tool which stimulates creative thinking can promote reflection, creativity, and self-efficacy in novice surgeons without compromising surgical performance.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".