Developments in Undergraduate Teaching of Small-Animal Soft-Tissue Surgical Skills at the University of Sydney
Bibliographic record
Abstract
This article discusses recent developments in soft-tissue surgery teaching at the University of Sydney, Faculty of Veterinary Science. An integrated teaching program was developed for Bachelor of Veterinary Science (BVSc) students with the aim of providing them with optimal learning opportunities to meet "Day One" small-animal soft-tissue surgical competencies. Didactic lectures and tutorials were introduced earlier into the curriculum to prepare students for live-animal surgery practical. In addition to existing clinics, additional spay/neuter clinics were established in collaboration with animal welfare organizations to increase student exposure to live-animal surgery. A silicon-based, life-like canine ovariohysterectomy model was developed with the assistance of a model-making and special effects company. The model features elastic ovarian pedicles and suspensory ligaments, which can be stretched and broken like those of an actual dog. To monitor the volume and type of student surgical experience, an E-portfolio resource was established. This resource allows for the tracking of numbers of live, student-performed desexing surgeries and incorporates competency-based assessments and reflective tasks to be completed by students. Student feedback on the integrated surgical soft-tissue teaching program was assessed. Respondents were assessed in the fourth year of the degree and will have further opportunities to develop Day One small-animal soft-tissue surgical competencies in the fifth year. Ninety-four percent of respondents agreed or strongly agreed that they were motivated to participate in all aspects of the program, while 78% agreed or strongly agreed that they received an adequate opportunity to develop their skills and confidence in ovariohysterectomy or castration procedures through the fourth-year curriculum.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".