Using Fine Arts–Based Training to Develop Observational Skills in Veterinary Students Learning Cytology: A Pilot Study
Bibliographic record
Abstract
Arts-based training has been shown to improve medical students’ observational skills. Veterinarians also need keen observational skills. Student veterinarians are expected to develop their observational skills; however, this training is usually not an explicit part of the veterinary curriculum. The impact of arts-based observation training has not been investigated in veterinary students learning cytology. In this pilot study, we compared student descriptions of art and cytology images before and immediately after receiving arts-based observation training. After 10 hours of cytology instruction, we again tested students’ observational skills and asked for feedback via a survey. Pre-tests and post-tests were scored following a rubric based on expert descriptions of the images. Scores for art image descriptions were higher for both the immediate and delayed post-tests compared to the pre-test ( p < .05). Scores for cytology image descriptions were higher for the immediate post-test than the pre-test, but this difference was not significant. Despite 10 hours of cytology instruction between post-tests, scores for cytology image descriptions were lower for the delayed post-test than the immediate post-test, but again, this difference was not significant. Student feedback on the arts-based observation training was positive. Overall, our results suggest that arts-based training may improve student observational skills, although context could be important, as the improvement in description was only significant for art images. Further investigation with a larger cohort of students and a control group that does not receive arts-based training would be valuable.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".