Motivation and Experience Matters: What Veterinary Mentors Think About Learning Communication Skills: A Qualitative Study
Bibliographic record
Abstract
Communication skills are a core competence in veterinary medicine. These skills play a pivotal role in professional success in the animal health professions. Over the last few decades, there has been an increased focus on communication skills in veterinary curricula. Conversely, we know less about the knowledge and motivation behind the communication skills of those veterinarians in different work domains who are acting as mentors outside the university. In 2016, semi-structured interviews ( n = 16) were conducted with German practitioners in workplaces ranging from companion to farm animal practice, and throughout the veterinary industry, veterinary research, and government service. We combined two qualitative methods: a thematic analysis approach and the generation of types to identify characteristics associated with the acquisition of communication skills. In the current study, three main themes were developed: “Motivation,” “Experiences with the acquisition of communication skills,” and “Communication skills training during formal education.” Within the identified themes, we recognized three types of communicators: “self-experienced,” “extrinsic-experienced,” and “unexperienced.” We found that acquisition of communication skills was closely linked to motivation; therefore, motivation must be considered when developing communication skills curricula for learners and educators. By extrapolating the findings of this explorative study, we determined that intrinsically motivated mentors from the field should be a main source of veterinary education to promote further development in communication training. This qualitative study also determined that most non-university veterinary mentors had only a basic knowledge of teaching and learning communication skills, leading us to recommend formal training. Interchange between practicing veterinarians and veterinary educators and curriculum coordinators can foster relevant curricular modifications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".