Effect of Short- Versus Long-Term Video Game Playing on Basic Laparoscopic Skills Acquisition of Veterinary Medicine Students
Bibliographic record
Abstract
The objective of this study was to investigate the effect of playing video games on the performance of basic laparoscopic skills. The study was an experimental pre-test–post-test comparison group design. Fifty-two students (31 from the Western University of Health Sciences College of Veterinary Medicine and 21 from the Colorado State University College of Veterinary Medicine) completing their first or second year of the veterinary curriculum were randomized into two intervention groups. The intervention consisted of playing the video game Marble Mania on a Nintendo Wii. group L (long) played 18 hours over 6 weeks and group S (short) played 3 hours during the last week of the 6-week intervention period. Before and after the intervention, basic laparoscopic skills for both groups were assessed using a modified McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS) instrument. Participants performed two laparoscopic tasks. Results showed that the performance of students improved in both group S and L ( p < .05) on both laparoscopic tasks. Both groups showed statistically significant improvement in their post-intervention scores (group L, N = 25, z = –3.711, p < .001, r = 0.742; group S, N = 27, z = –3.016, p < .003, r = 0.580). There was no significant difference in the degree of improvement between group S and group L. The results suggest that playing Marble Mania on a Wii for any time duration could be an effective method for veterinary medicine students to improve basic laparoscopic skills, and indicate the needs for additional studies.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".