Competency Frameworks: Which Format for Which Target?
Bibliographic record
Abstract
In veterinary medical education, it is now necessary to design competency frameworks (CFs) that list expected competencies at graduation. Three different CFs with different formats and contents have been published in Europe, such as the Day One Skills (DOS), the recommendations of the World Organization for Animal Health(OIE), and the Veterinary Professional (VetPro). In the current study, on the basis of a survey among Belgian veterinarians, a fourth document was designed that lists the necessary knowledge, skills, and attitudes grouped into families according to professional situations. The objectives of this study were to assess the perception of CFs by various categories of stakeholders, identify the possible uses of CFs, and determine whether one format should be preferred to another. We used a qualitative approach based on semi-structured face-to-face interviews with different stakeholders after they had reviewed the four different documents (CFs). This study showed that an obligation to design CFs was clearly perceived by academic and professional authorities. Teachers and veterinarians may be either enthusiastic or apprehensive about CFs, while students perceive the usefulness of the documents to plan and assess their learning objectives. Three main roles of CFs were identified: they can be used as communication tools, regulatory tools, or educational tools. However, not one of the documents used in this study was perceived to fulfill all roles. It is therefore likely that no one ideal document yet exists and a combination of formats is necessary.
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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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| 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".