Exploration of Immersive Virtual Reality in Teaching Veterinary Orthopedics
Bibliographic record
Abstract
The COVID-19 pandemic has catalyzed the use of novel teaching modalities to enhance the provision of remote veterinary education. In this study, we describe the use of immersive virtual reality (iVR) as a teaching aid for veterinary medicine students during their orthopedics clinical rotation. Student sentiments were assessed using voluntary electronic surveys taken by veterinary students before and after the rotation. The most noteworthy benefits students reported were improved engagement with the course content, information retention, radiographic interpretation, and clinical reasoning skills. Obstacles encountered during the initial stages of the program included financial and temporal investment in equipment and content development, technical troubleshooting, and motion sickness. Though it is unlikely that iVR will ever fully replace hands-on learning experiences, it presents an educational opportunity to supplement traditional learning methods, motivate students, and fill information gaps. As iVR technology continues to evolve and improve, potential applications in the veterinary curriculum grow, making the modality's use progressively more advantageous. Although this study describes its application in an orthopedic setting, the versatility of the iVR modality lends the potential for it to be implemented in a number of clinical and didactic settings.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".