Exploring the Use of a Novel Self-Assessment Employability Questionnaire to Evaluate Undergraduate Veterinary Attainment of Professional Attributes: An Explanatory Mixed-Methods Study
Bibliographic record
Abstract
The move to outcome-based education for professional degrees has placed an emphasis on defining what constitutes competencies for a profession. A review of the literature on professional competencies shows the development of professional frameworks that encompass the knowledge, clinical skills, professional skills, and professional attributes regarded as necessary for veterinary graduates. It follows that veterinary education has a responsibility to ensure students have these professional competencies. This study used an explanatory mixed-methods approach to determine whether veterinary students at the University of Glasgow attained professional skills and attributes. Using a publicly available employability framework developed as part of the VetSet2Go project, a quantitative comparison was made between students of different genders and students from separate year groups. Focus groups from these year groups explored the potential reasons for the scores and where the employability attributes were acquired. Participants were asked to provide feedback on the usefulness of the employability self-assessment tool. Data analysis showed that students tended to score themselves low on self-confidence and high on trustworthiness. Fourth-year students tended to score themselves lower on each attribute than second-year students. Results indicate that students are aware of the provision of teaching interventions for the development of certain attributes, but they feel some attributes are gained through experience and recognize the importance of school culture; university provides a period for socialization in a professional identity. Self-confidence is important for well-being and for bringing value to future employers, and educators should consider ways to improve this attribute.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".