Expectations of Graduate Communication Skills in Professional Veterinary Practice
Bibliographic record
Abstract
Good communication skills are an important entry-level attribute of graduates of professional degrees. The inclusion of communication training within the curriculum can be problematic, particularly in programs with a high content load, such as veterinary science. This study examined the differences between the perceptions of students and qualified veterinarians with regards to the entry-level communication skills required of new graduates in clinical practice. Surveys were distributed to students in each of the four year levels of the veterinary science degree at the University of Melbourne and to recent graduates and experienced veterinarians registered in Victoria, Australia. Respondents were asked to rank the relative importance of six different skill sets: knowledge base; medical and technical skills; surgical skills; verbal communication and interpersonal skills; written communication skills; and critical thinking and problem solving. They were then asked to rate the importance of specific communication skills for new graduate veterinarians. Veterinarians and students ranked verbal communication and interpersonal skills as the most important skill set for an entry-level veterinarian. Veterinarians considered many new graduates to be deficient in these skills. Students often felt they lacked confidence in this area. This has important implications for veterinary educators in terms of managing the expectations of students and improving the delivery of communication skills courses within the veterinary curriculum.
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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 | 0.005 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".