Evaluation of expectation, learning and competency of surgical skills by veterinary students performing canine ovariohysterectomies
Bibliographic record
Abstract
OBJECTIVES: To investigate the development of surgical skills of veterinary undergraduates and determine the number of canine ovariohysterectomies required to achieve competency and reduce levels of student concern. This was compared to student expectations and that of employers regarding surgical ability and provision of support to new graduates. METHODS: A questionnaire regarding surgical concerns was sent to final year veterinary students enrolled within the University of Bristol, UK. A questionnaire was also sent to 200 UK veterinary practices regarding their impressions of surgical competence of new graduates and their provision of supervision. The responses were compared. Eleven additional final year students performed five canine ovariohysterectomies and graded their concerns. The number of supervised canine ovariohysterectomies required until competency was determined. RESULTS: 80·4% of final year veterinary undergraduates replied that the surgical procedure which they were most concerned about their ability to perform was canine ovariohysterectomy. Students and veterinary practitioners differed in their opinions regarding whether they considered canine ovariohysterectomy to be a "day one skill" and what were desirable levels of supervision. Completing a minimum of four canine ovariohysterectomies led to 81·8% of students being assessed as competent. CLINICAL SIGNIFICANCE: An unrealistically high expectation of competency by students may be a source of stress and concern. Employers should aim to provide hands-on support whilst new graduates complete at least four canine ovariohysterectomies. Postoperative haemorrhage is uncommon but is the main concern for students.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".