Comparison of Fine Arts– and Pathology-Based Observational Skills Training for Veterinary Students Learning Cytology
Bibliographic record
Abstract
Keen observational skills are essential for veterinarians; however, the development of these skills is not usually an explicit part of the veterinary curriculum. Fine arts-based (FAB) observation training has been shown to improve medical students' observational skills and might also improve veterinary students' observational skills. We compared FAB and pathology-based (PB) observation training in a veterinary cytology course. Students initially wrote a pre-test in which they described two cytology images and one art image, followed by participation in either FAB or PB observation training. Both groups completed a similar post-test immediately after training and a delayed post-test 4 weeks later following instruction in cytology. Differences between groups were noted only in the immediate post-test cytology descriptions. The PB group used significantly more specific vocabulary terms and significantly more accurate observations than the FAB group, suggesting an immediate benefit to the discipline-specific information gained in the PB observation training. In the delayed post-test, results for both groups were similar. The FAB group significantly increased their use of specific vocabulary terms and maintained but did not increase accurate observations following cytology instruction, while accurate observations decreased significantly for the PB group. The FAB group might have been able to generalize their observation skills to the discipline of cytology and to better retain these skills. Neither type of training resulted in both achievement and maintenance of the highest recorded scores for accurate observations. Both FAB and PB training led to improved observational skills, and explicit observation training may be useful for veterinary students.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".