Preparing Veterinary Students for Extramural Clinical Placement Training: Issues Identified and a Possible Solution
Bibliographic record
Abstract
Extramural clinical placement training is an important part of many veterinary degree programs and provides students with valuable learning experiences in private practice, often focusing on the management of typical first-opinion cases. In the United Kingdom, the Royal College of Veterinary Surgeons (RCVS) has a mandatory requirement that students take 26 weeks of clinical placement or extramural studies (EMS) before graduation. However, if students are to maximize their learning opportunities during these placements, it is important that they be adequately prepared. In response to recent topical issues surrounding EMS in the United Kingdom, the current project undertook an iterative consultation process with key stakeholders, including students and placement providers, to identify key issues associated with students attending placements. These findings then informed the development and content of a computer-aided learning (CAL) package titled "The EMS Driving Licence" that aimed to improve the preparation of students for placements. The CAL package included sections covering the main identified areas of concern: Preparation (including what to take), Working With People (staff and clients), Professionalism (including confidentiality), Frequently Asked Questions (from students), and Top Tips (from practitioners). The CAL package was evaluated by students, and feedback was gathered by means of a questionnaire. Students recognized that the content addressed many of their concerns, and all reported that they would recommend the package to others. The CAL package has been made available to all UK veterinary schools and has received backing from the RCVS as part of their current recommendations on EMS to the UK veterinary profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".