Development and Validation of a Competency Framework for Veterinarians
Bibliographic record
Abstract
Changing demands from society and the veterinary profession call for veterinary medical curricula that can deliver veterinarians who are able to integrate specific and generic competencies in their professional practice. This requires educational innovation directed by an integrative veterinary competency framework to guide curriculum development. Given the paucity of relevant information from the veterinary literature, a qualitative multi-method study was conducted to develop and validate such a framework. A competency framework was developed based on the analysis of focus group interviews with 54 recently graduated veterinarians and clients and subsequently validated in a Delphi procedure with a panel of 29 experts, representing the full range and diversity of the veterinary profession. The study resulted in an integrated competency framework for veterinary professionals, which consists of 16 competencies organized in seven domains: veterinary expertise, communication, collaboration, entrepreneurship, health and welfare, scholarship, and personal development. Training veterinarians who are able to use and integrate the seven domains in their professional practice is an important challenge for today's veterinary medical schools. The Veterinary Professional (VetPro) framework provides a sound empirical basis for the ongoing debate about the direction of veterinary education and curriculum development.
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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 | 0.067 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".