Beyond Competence: Why We Should Talk About Employability in Veterinary Education
Bibliographic record
Abstract
The purpose of this article is to explore employability as a complement to competency in defining the overarching objectives of veterinary education. Although the working usage of the term competency has evolved and stretched in recent years, and contemporary competence frameworks have expanded to better reflect the range of capabilities required of a veterinary professional, the potential remains for the dominance of competency-led discourse to obscure the aim of producing not only competent but also successful and satisfied veterinarians. Expanding the educational mission to include employability may provide this broader focus, by stretching the end point, scope, and scale of veterinary education into the crucial transition-to-practice period, and beyond. In this article we review available evidence from multiple stakeholder perspectives and argue that employability expands the focus beyond servicing the needs of the public to better integrate and balance the needs of all the stakeholders in veterinary education, including the graduates themselves. By refocusing the goal of veterinary education to include the richer end point of success, turning the attention to employability could enhance current attribute frameworks and result in veterinarians who not only better meet the needs of those they serve but are also better prepared to experience fulfilling and satisfying careers. Finally, we suggest one educational approach may be to conceptualize competency, professionalism, and employability as overlapping dimensions of the successful veterinary professional.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.064 |
| Scholarly communication | 0.017 | 0.039 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".