Teamwork Training With a Multiplayer Game in Health Care: Content Analysis of the Teamwork Principles Applied
Bibliographic record
Abstract
BACKGROUND: In health care, teamwork skills are critical for patient safety; therefore, great emphasis is placed on training these skills. Given that training is increasingly designed in a blended way, serious games may offer an efficient method of preparing face-to-face simulation training of these procedural skills. OBJECTIVE: This study aimed to investigate the teamwork principles that were used during gameplay by medical students and teamwork experts. Findings can improve our understanding of the potential of serious games for training these complex skills. METHODS: We investigated a web-based multiplayer game designed for training students' interprofessional teamwork skills. During gameplay, 4 players in different roles (physician, nurse, medical student, and student nurse) had to share information, prioritize tasks, and decide on next steps to take in web-based patient scenarios, using one-to-one and team chats. We performed a qualitative study (content analysis) on these chats with 144 fifth-year medical students and 24 health care teamwork experts (as a benchmark study) playing the game in groups of 4. Game chat data from 2 scenarios were analyzed. For the analysis, a deductive approach was used, starting with a conceptual framework based on Crew Resource Management principles, including shared situational awareness, decision-making, communication, team management, and debriefing. RESULTS: Results showed that most teamwork principles were used during gameplay: shared situational awareness, decision-making (eg, re-evaluation), communication (eg, closed loop), and team management (eg, distributing the workload). Among students, these principles were often used on a basic level. Among experts, teamwork principles were used with more open forms of speak up and more justification of decisions. Some specific Crew Resource Management principles were less observed among both groups, for example, prevention of fixation errors and use of cognitive aids. Both groups showed relatively superficial debriefing reflections. CONCLUSIONS: Playing a multiplayer game for interprofessional teamwork appears to facilitate the application of teamwork principles by students in all important teamwork domains on a basic level. Expert players applied similar teamwork principles on a moderately high complexity level. Some teamwork principles were less observed among both students and expert groups, probably owing to the artifacts of the game environment (eg, chatting instead of talking). A multiplayer game for teamwork training can elicit the application of important, basic teamwork principles, both among novices and experts, and provides them with a flexible, accessible, and engaging learning environment. This may create time for exercising more complex skills during face-to-face training.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".