Building Veterinarians beyond Veterinary School: Challenges and Opportunities for Continuing Professional Development in Veterinary Medicine
Bibliographic record
Abstract
Continuing professional development (CPD) is an important mechanism for veterinarians to acquire, maintain, and enhance their capability to perform competently in their chosen practice area over their career. Although most licensing bodies require veterinarians to complete a minimum number of CPD hours each registration cycle, there are known issues with verifying that these activities are having the desired effects of ensuring professional competence and improving outcomes for patients, owners, and veterinarians. In this review, we summarize the literature across different health care professions to highlight three key challenges for veterinary CPD programs. These are (a) defining what it means to be professionally competent across different career stages from graduation to retirement, (b) delivering CPD activities that are effective in promoting evidence-based medicine and behavioral change in practice, and (c) developing reliable and sustainable systems to formally assess the continued professional competence of veterinarians. A common theme across all challenges was the importance of interacting with colleagues as an opportunity to receive external feedback on their professional strengths and weaknesses and to develop stronger support networks for managing common stressors in clinical practice. There was also a recognized need to develop more transparent outlines of the available and acceptable options for managing different animal health concerns as a first step toward identifying new opportunities for the veterinary profession to elevate the level of care provided. Based on these findings, we propose a new framework for defining, delivering, and evaluating CPD that promotes stronger collaboration between veterinarians to improve professional and personal well-being.
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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 | 0.041 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".