Construct Validity of a Serious Game for Laparoscopic Skills Training: Validation Study
Bibliographic record
Abstract
Background Surgical residents underutilize opportunities for traditional laparoscopic simulation training. Serious gaming may increase residents’ motivation to practice laparoscopic skills. However, little is known about the effectiveness of serious gaming for laparoscopic skills training. Objective The aim of this study was to establish construct validity for the laparoscopic serious game Underground. Methods All study participants completed 2 levels of Underground. Performance for 2 novel variables (time and error) was compared between novices (n=65, prior experience <10 laparoscopic procedures), intermediates (n=26, prior experience 10-100 laparoscopic procedures), and experts (n=20, prior experience >100 laparoscopic procedures) using analysis of covariance. We corrected for gender and video game experience. Results Controlling for gender and video game experience, the effects of prior laparoscopic experience on the time variable differed significantly (F2,106=4.77, P=.01). Both experts and intermediates outperformed novices in terms of task completion speed; experts did not outperform intermediates. A similar trend was seen for the rate of gameplay errors. Both gender (F1,106=14.42, P<.001 in favor of men) and prior video game experience (F1,106=5.20, P=.03 in favor of experienced gamers) modulated the time variable. Conclusions We established construct validity for the laparoscopic serious game Underground. Serious gaming may aid laparoscopic skills development. Previous gaming experience and gender also influenced Underground performance. The in-game performance metrics were not suitable for statistical evaluation. To unlock the full potential of serious gaming for training, a more formal approach to performance metric development is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".