A Web-Based Serious Game on Delirium as an Educational Intervention for Medical Students: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Adequate delirium recognition and management are important to reduce the incidence and severity of delirium. To improve delirium recognition and management, training of medical staff and students is needed. OBJECTIVE: In this study, we aimed to gain insight into whether the serious game, Delirium Experience, is suited as an educational intervention. METHODS: We conducted a three-arm randomized controlled trial. We enrolled 156 students in the third year of their Bachelor of Medical Sciences degree at the University Medical Centre Groningen. The Game group of this study played Delirium Experience. The Control D group watched a video with explanations on delirium and a patient's experience of delirious episodes. The Control A group watched a video on healthy aging. To investigate students' skills, we used a video of a delirious patient for which students had to give care recommendations and complete the Delirium Observations Screening Scale and Delirium Rating Scale R-98. Furthermore, students completed the Delirium Attitude Scale, the Learning Motivation and Engagement Questionnaire, and self-reported knowledge on delirium. RESULTS: In total, 156 students participated in this study (Game group, n=51; Control D group, n=51; Control A group, n=55). The Game group scored higher with a median (interquartile range) of 6 (4-8) for given recommendations and learning motivation and engagement compared with the Control D (1, 1-4) and A (0, 0-3) groups (P<.001). Furthermore, the Game group scored higher (7, 6-8) on self-reported knowledge compared with the Control A group (6, 5-6; P<.001). We did not find differences between the groups regarding delirium screening (P=.07) and rating (P=.45) skills or attitude toward delirious patients (P=.55). CONCLUSIONS: The serious game, Delirium Experience, is suitable as an educational intervention to teach delirium care to medical students and has added value in addition to a lecture.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".