Investigating laparoscopic psychomotor skills in veterinarians and veterinary technicians
Bibliographic record
Abstract
Abstract Objectives To determine the influence of age, year of graduation, and video game experience on baseline laparoscopic psychomotor skills. Study design Cross‐sectional. Sample Population : Licensed veterinarians (n = 38) and registered veterinary technicians (VTs) (n = 49). Methods A laparoscopic box trainer was set up at the 2016 Ontario Veterinary Medical Association (OVMA) and the 2016 Ontario Association of Veterinary Technicians (OAVT) conferences held in Toronto, Ontario, Canada. Participants volunteered to perform a single repetition of a peg transfer (PT) exercise. Participants were given a short demonstration of the PT task prior to testing. A Spearman's rank correlation (r s ) was used to identify associations between baseline psychomotor skills and self‐reported surgical and non‐surgical experiences collected via survey. Mann‐Whitney U tests were used to compare PT scores in veterinarians and VTs. A P ‐value of < .05 was considered significant. Results The mean age of participants was 36 years (range 21‐67) and the majority were female (83%). In veterinarians, PT scores were highest in the most recent graduates ( P = .01, r s = 0.42), and PT scores increased with self‐reported VG experience ( P = .02, r s = 0.38). PT scores correlated inversely with age ( P = .02, r s = −0.37). No associations were observed in VTs ( P > .05). Veterinary technicians that frequently used chopsticks scored higher than those without chopstick experience ( P = .04). Conclusions Age and year of graduation correlated inversely, while self‐reported VG experience correlated positively with laparoscopic psychomotor skills of veterinarians, when assessed on a simulator. The use of chopsticks may contribute to the acquisition of psychomotor skills in VTs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".