Analysis of Turkish Veterinary Students’ Self-perception of Communication Competencies Based on Gender Differences
Bibliographic record
Abstract
Communication skills are teachable and learnable skills, which have a vital position among other clinical skills because a student’s ability to communicate can increase empathy. The focus of this article is to determine how senior students evaluate themselves regarding communication competence and whether gender has an impact on their perception. The study included 128 volunteering students, using the Communication Competence Scale, consisting of 30 questions, as a data collection tool and the independent samples t-test for statistical evaluations. The evaluation of all participants showed that male participants had the highest score, and female participants had the lowest. However, there was no statistically significant difference between female and male participants’ total scores ( p = 0.605). There was a statistically significant difference between female and male students in terms of the social competency, empathy, and adaptability. Female scores for empathy were statistically higher than those of males. Male students scored themselves higher than females in terms of social competency and adaptability. In the context of the students’ perceptions of their communication competence, it was determined that females assessed themselves to be more empathetic and males perceived themselves to be more social and adaptable. This research is significant as it is the first study of Turkish veterinary students’ self-perception of communication competence. Communication training may become more robust in veterinary curricula in Turkey, and further research will be affected by this issue.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".