Serious Games for Improving Technical Skills in Medicine: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Serious games are being used to train specific technical skills in medicine, and most research has been done for surgical skills. It is not known if these games improve technical skills in real life as most games have not been completely validated. OBJECTIVE: This scoping review aimed to evaluate the current use of serious games for improving technical skills in medicine and to determine their current validation state using a validation framework specifically designed for serious games. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. A multidatabase search strategy was adopted, after which a total of 17 publications were included in this review. RESULTS: These 17 publications described five different serious games for improving technical skills. We discuss these games in detail and report about their current validation status. Only one game was almost fully validated. We also discuss the different frameworks that can be used for validation of serious games. CONCLUSIONS: Serious games are not extensively used for improving technical skills in medicine, although they may represent an attractive alternative way of learning. The validation of these games is mostly incomplete. Additionally, several frameworks for validation exist, but it is unknown which one is the best. This review may assist game developers or educators in validating serious games.
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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 | 0.019 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".