Gamification in the Design of Virtual Patients for Swedish Military Medics to Support Trauma Training: Interaction Analysis and Semistructured Interview Study
Bibliographic record
Abstract
BACKGROUND: This study explores gamification in the design of virtual patients (VPs) to enhance the training of Swedish military medics in trauma care. The challenges related to prehospital trauma care faced on the battlefield require tailored educational tools that support military medics' education and training. OBJECTIVE: The aim of the study is to investigate how to design VPs with game elements for Swedish military medics to support learning in military trauma care. By understanding the reasoning and perceptions of military medics when interacting with VPs, this study aims to provide insights and recommendations for designing VPs with game elements that are specifically tailored to their needs. METHODS: The study involved 14 Swedish military medics of the Home Guard-National Security Forces participating in a tactical combat care course. Participants interacted with 3 different VP cases designed to simulate military trauma scenarios. Data were collected through think-aloud sessions and semistructured interviews. The data were analyzed using interaction analysis, structured by the unawareness, problem identification, explanation, and alternative strategies or solutions (uPEA) framework, and reflexive thematic analysis to explore participants' reasoning processes and perceptions and identify possible game elements to inform the VP design. RESULTS: Mapping the military medics' reasoning to the uPEA framework revealed that study participants became more creative after making a mistake followed by feedback and after receiving a prompt to make a new decision. The thematic analysis revealed 6 themes: motivation, "keep on trying"; agency in interaction with VPs; realistic tactical experience; confidence, "I know that the knowledge I have works"; social influence on motivation; and personalized learning. Participants suggested that game elements such as scoring; badges; virtual goods; progress bars; performance tables; content unlocking; hints; challenge; control; imposed choice; narrative; avatars; sensation; randomness; difficulty adapting; competition; leaderboards; social pressure; progression; and renovation can promote engagement, motivation, and support confidence in decision-making. CONCLUSIONS: Gamification in the design of VPs represents a promising approach to military medical training, offering a platform for medics to practice medical and tactical decision-making in a risk-free environment. The insights gained by the study may encourage designing VPs with game elements, as well as including possibly wrong decisions, their consequences, and relevant feedback, that may support military medics' reflections and decision-making.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".