Effect of a Spay Simulator on Student Competence and Anxiety
Bibliographic record
Abstract
Spay simulation has gained attention at colleges of veterinary medicine that seek to utilize low-cost models in lieu of more cost-prohibitive high-fidelity devices or cadaveric specimens. A spay simulator was developed to provide veterinary students at the University of Florida College of Veterinary Medicine a reusable, inexpensive, and error-enabled device for self-practice in anticipation of a live canine ovariohysterectomy. Seventy-four students were recruited, half of whom participated in spay simulation training. A survey was designed to capture students' state and trait anxiety, as well as their self-assessed perceived levels of competence, confidence, and knowledge of anatomy, before and after their live animal surgery. During the live surgical laboratories, surgical competencies were assessed using the Objective Structured Assessment of Technical Skills (OSATS) for operative performance. We hypothesized that the spay simulation training group would have higher reported levels of competence, confidence, and knowledge of anatomy. Additionally, students enrolled in spay simulation training were expected to exhibit a lower level of post-operative anxiety and higher OSATS scores compared with the control group. Results demonstrated a significant increase in perceived anatomical knowledge and improvement in perceived competence level following spay simulation training as compared with the control group. Areas of no difference included perceived confidence, OSATS scores, and overall level of anxiety. The results of this study demonstrate that this low-fidelity spay simulator has a unique place in student surgical training, producing novice surgeons with increased perceived competence and knowledge of anatomy following spay simulation training and live animal surgery.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".