Client Perspectives on Desirable Attributes and Skills of Veterinary Technologists in Australia: Considerations for Curriculum Design
Bibliographic record
Abstract
Client or service user perspectives are important when designing curricula for professional programs. In the case of veterinary technology, an emerging profession in the veterinary field in Australasia, client views on desirable graduate attributes, skills, and knowledge have not yet been explored. This study reports on a survey of 441 veterinary clients (with 104 responses) from four veterinary practices in Brisbane, Queensland, conducted between October 2008 and February 2009. The included veterinary practices provided clinical placements for veterinary technology undergraduates and employment for veterinary technology graduates (2003-2007). Client socio-demographic data along with ratings of the importance of a range of technical (veterinary nursing) skills, emotional intelligence, and professional attributes for veterinary technology graduates were collected and analyzed. Overall, the majority of clients viewed technical skills, emotional intelligence, and professional attributes as important in the clinical practice of veterinary technology graduates with whom they interacted in the veterinary practice. Client interviews (n=3) contextualized the survey data and also showed that clients attached importance to graduates demonstrating professional competence. Agglomerative hierarchical cluster analysis revealed four distinct groupings of clients within the data based on their differing perceptions. Using a multivariable proportional-odds regression model, it was also found that some client differences were influenced by demographic factors such as gender, age, and number of visits annually. For example, the odds of female clients valuing emotionality and sociability were greater than males. These findings provide useful data for the design of a professionalizing and market-driven veterinary technology curriculum.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".