Integrating a Bovine Rectal Palpation Simulator into an Undergraduate Veterinary Curriculum
Bibliographic record
Abstract
There are problems associated with teaching bovine rectal palpation to undergraduate veterinary students. The students need opportunities to examine enough cows to develop the required skills, but increasing student numbers and limitations on access to cows have made this more and more difficult to achieve. A virtual reality-based teaching tool, the Bovine Rectal Palpation Simulator, has been developed as a supplement to existing training methods. The student palpates computer generated virtual models of the bovine reproductive tract while interacting with a haptic (touch feedback) device. During training sessions, the instructor follows the student's actions inside the virtual cow on the computer screen and gives instruction. A trial integration of the simulator into the fourth-year bovine reproduction course was undertaken at the University of Glasgow Veterinary School during the 2003/2004 academic year. Students were offered two training sessions, and feedback was gathered using questionnaires. In the first session, all students were taught a range of basic skills using a standardized teaching protocol. The second training session was customized to each student's learning needs and included practice in dealing with a range of on-farm scenarios. Student feedback indicated that the training had been useful for learning various aspects of bovine rectal palpation and provided information that helped in the further development of the simulator as a teaching tool.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".