First Experimental Study in Turkey Teaches Veterinary Students How to Break Bad News
Bibliographic record
Abstract
The importance of communication skills in veterinary medicine has been increasing for a long time. The aim of this article is to investigate how theoretical training, role-playing, and standardized/simulated client (SC) methods improve senior (fifth-year) veterinary students’ skills in breaking bad news. The study was carried out with 67 volunteer senior students. The research was designed from a pre-test and post-test control group pattern. All students encountered the SC. After pre-tests, theoretical training was given to Experimental Group A (EGA) and Experimental Group B (EGB). Then, only the students in EGA role-played together. Each student completed a checklist consisting of 10 basic items after pre-tests and post-tests. After post-tests, focus group interviews with open-ended questions were conducted. In the pairwise comparisons, EGA’s and EGB’s adjusted post-test mean scores were significantly higher than the control group’s ( p < .001). EGA’s and EGB’s post-test scores were found to be significantly higher than their pre-test scores. Women’s empathy and eye contact scores were found to be statistically higher than men’s scores. This study is the first of its kind in Turkey to use SCs and peer-to-peer learning with role-play simulations in training students about breaking bad news in veterinary medicine. These findings show that theoretical training and role-playing has an impact on senior veterinary students’ skills in breaking bad news.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".