A Serious Game for Enhancing Rescue Reasoning Skills in Tactical Combat Casualty Care: Development and Deployment Study
Bibliographic record
Abstract
Background Serious games (SGs) have emerged as engaging and instructional digital simulation tools that are increasingly being used for military medical training. SGs are often compared with traditional media in terms of learning outcomes, but it remains unclear which of the 2 options is more efficient and better accepted in the process of knowledge acquisition. Objective This study aimed to create and test a scenario-based system suitable for enhancing rescue reasoning skills in tactical combat casualty care. Methods To evaluate the effectiveness of the SGs, a randomized, observational, comparative trial was conducted. A total of 148 members from mobile medical logistics teams were recruited for training. Pre- and posttraining assessments were conducted using 2 different formats: a video-based online course (n=78) and a game simulation (n=70). We designed 3 evaluation instruments based on the first 2 levels of the Kirkpatrick model (reaction and learning) to measure trainees’ satisfaction, knowledge proficiency, and self-confidence. Results There were 4 elements that made up the learning path for the SGs: microcourses (video-based online courses), self-test, game simulation, and record query. The knowledge test scores in both groups were significantly higher after the intervention (t154=–6.010 and t138=–7.867, respectively; P<.001). For 5 simulation cases, the average operation time was 13.6 (SD 3.3) minutes, and the average case score was 279.0 (SD 57.6) points (from a possible total of 500 points), with a score rate of only 44% (222/500 points) to 67% (336/500 points). The results indicated no significant difference in trainees’ satisfaction between the 2 training methods (P=.04). However, the game simulation method outperformed the video-based online course in terms of learning proficiency (t146=–2.324, P=.02) and self-perception (t146=–5.492, P<.001). Conclusions Despite the high satisfaction reported by trainees for both training methods, the game simulation approach demonstrated superior efficiency and acceptance in terms of knowledge acquisition, self-perception, and overall performance. The developed SG holds significant potential as an essential assessment tool for evaluating frontline rescue skills and rescue reasoning in mobile medical logistics teams.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".