The Development and Implementation of a National Veterinary Graduate Development Programme (VetGDP) to Support Veterinarians Entering the UK Workforce
Bibliographic record
Abstract
The UK veterinary profession is facing significant challenges, including high rates of veterinarians leaving the profession amid workforce shortages, alongside high levels of dissatisfaction, stress, and poor mental health. The highest rates of attrition are associated with recently graduated veterinarians who are at an early stage in their career. Although there may be many contributory factors, a lack of adequate support during the transition from vet school into their first professional role following graduation may be one important cause. Consequently, it has never been more important to develop an effective system for supporting new graduates that is accessible to all. A new Veterinary Graduate Development Programme (VetGDP) has been developed, using a framework of professional activities that are sufficiently flexible to create a bespoke, individualized program for each graduate depending on the role they enter. Each new graduate is assigned a dedicated coach (Adviser) within their workplace, who has been trained to provide effective support and has committed to doing so throughout the program. VetGDP has been implemented on a national scale in the UK; engagement is assured through the RCVS Code of Professional Conduct and quality assurance is in place. VetGDP has been developed using established educational, sociocultural, and behavioral theories, and the latest research in coaching and feedback within medical education. These methods, which aim to ensure there is the best possible impact on graduates' professional development, and the creation of a positive learning culture within the workplace, are taught to all Advisers via an e-learning package.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".