Mentoring New Veterinary Graduates for Transition to Practice and Lifelong Learning
Bibliographic record
Abstract
A new veterinarian's smooth and rapid transition from education to clinical practice is critical to their success and that of their new professional homes. Successful mentoring relationships are critical to smoothing the transition to practice, particularly when independent clinical decisions are abruptly required. A mentor acts as a personal coach and teacher, providing both career and personal guidance. While the profession has focused on training mentors, it has paid little attention to teaching mentees how to maximize the benefits of the relationship. Veterinary colleges can do more to equip their graduates with the skills they need to manage their change to working life successfully. The Western College of Veterinary Medicine's (WCVM) substantive gap analysis revealed mentee training as an important issue to address in support of mentorship and established a mentee training program within the curriculum. The program teaches needs assessment, goal setting, identification of appropriate learning activities, and reflection skills as an iterative and cyclical process. Learning activities include working with one's selected mentor (or mentors). These skills are important for lifelong learning and continuing professional development, as well as transition to practice.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".