Experience of Practicing Veterinarians with Supervising Final-Year Students and New Graduates in Performing Desexing Surgeries
Bibliographic record
Abstract
With increasing pressure on university teaching hospital caseloads, veterinary students are increasingly being taught basic desexing skills during their final-year extramural rotations or as new graduates in practice. A cross-sectional survey of New Zealand veterinarians was conducted to elicit information about their experiences supervising these cohorts. Of the 162 respondents who had supervised at least one final-year veterinary student, only 95 (58.6%) allowed students to perform desexing surgeries and the most common procedures they allowed students to perform were cat neuters (96%) followed by cat spays (64%), dog neuters (63%), and dog spays (24%). The time needed to supervise students, the liability of students operating on client-owned animals, and students' poor basic instrument, tissue, and suture handling skills were cited as major deterrents. Breaks in sterility and dropped pedicles were the most frequently reported complications, although these still occurred only occasionally or rarely. Of the 101 respondents who had supervised at least one new graduate, all but one provided surgical mentoring. It took an average of 3.3 dog neuters, 8 dog spays, 2.4 cat neuters, and 4.7 cat spays before respondents were comfortable letting new graduates perform surgery unassisted. Respondents generally expected new graduates to perform dog spays in under 60 minutes, cats spays and dog neuters in under 30 minutes, and cat neuters in under 10 minutes. Although most respondents agreed that students needed more hands-on experience with live animal surgery, the main clinical skills deficiencies identified were ones that could easily be trained and practiced on simulated models.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".