Veterinary technology/nursing student perceptions of an experiential simulated client communication workshop
Bibliographic record
Abstract
Effective communication skills are highly desirable attributes for veterinary support personnel. These skills can be developed through experiential learning activities. This study evaluated the impact of an experiential simulated client communication workshop on final year veterinary technology/veterinary nursing student perceptions of competence related to a variety of communication skills by administering a pre- and post-workshop questionnaire. In the workshop, students had the opportunity to interact with actors playing the roles of clients within the context of common veterinary practice scenarios. Each interaction was followed by personal reflection from the student and peer, actor and facilitator feedback based on a student-led agenda. Following completion, when compared with pre-workshop responses, students were significantly more confident that they could utilize a range of professional and relationship-centred communication skills of relevance to veterinary practice. Almost all respondents indicated that the workshop was an enjoyable and valuable learning experience that helped to prepare them for the ‘real world’ following graduation. Results from this study may be of interest to institutions developing or enhancing strategies used for client communication skills training for veterinary support personnel.
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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 | 0.006 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".