“Clinical Teachers: Teaching Tips for the Busy Veterinary Team”: Reflections on the Development of a Multimodal Resource for Veterinarians and Veterinary Nurses/ Technologists New to Clinical Teaching
Bibliographic record
Abstract
Clinical practice-based training/work-integrated learning is an applied, social, and high-impact element of the veterinary curriculum. Within this context, students are learning on the job with clinician-educators who are carrying out their professional duties at the same time as supporting learning. To equip clinician-educators with role awareness and general teaching skills, it is recommended that all have access to basic teacher training. However, delivering this training can be challenging to organize and potentially costly when busy, time-poor clinician-educators are distributed across many geographical locations. This Teaching Tip shares our insights about developing and delivering a set of novel clinical teacher resources for veterinarians and veterinary nurses/technologists new to clinical teaching. The resources, underpinned by the principles of participatory design, integrate contemporary clinical educational theories with practical strategies and are interwoven with video clips capturing staff and student perspectives on key topics. While initially focused on creating just an online resource, we ultimately produced an A6 ring-bound booklet version and face-to-face workshops. In this article, we unpack considerations involved in committing to such a project and designing and creating the resources. We hope that this information may be of use to others when developing similar resources.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".