Application of Gamification Teaching in Disaster Education: Scoping Review
Bibliographic record
Abstract
Background: With climate change, the number of natural disasters is increasing globally, and the resulting weather-related events lead to increased loss of life and property. Meanwhile, the significance of disaster education is becoming increasingly important. Despite natural disasters being hard to predict, people's responses to such events can be improved by education and training. Gamification, an innovative teaching method, has demonstrated great potential across various fields, including disaster education. Objective: We aimed to investigate the different application types of gamification in disaster education, focusing on nursing staff, medical professionals, university students, and disaster relief workers. Specifically, the goal was to identify the types of gamified teaching used in disaster education. Methods: This scoping review was conducted according to the Joanna Briggs Institute methodology. The Participants, Concept, Context (PCC) model was used to frame the inclusion criteria. We performed a systematic search of the relevant literature across the Cochrane Library, PubMed, CINAHL, Embase, Web of Science, CNKI, Wanfang, VIPC, and SinoMed databases. Articles published in Chinese and English were selected for the review. The search was conducted to identify literature published from the establishment of the respective databases to April 21, 2024. Two researchers independently screened the literature according to the inclusion and exclusion criteria and extracted the data. Results: We included a total of 16 studies in this review, originating from 8 different countries. These studies involved 1744 participants: nursing students (n=451), medical students from other majors (n=420), college students (n=287), hospital decision makers (n=264), hospital medical staff (n=262), and disaster relief workers (n=60). The gamification approaches for teaching and learning encompassed the following 7 categories: tabletop games, serious games, scenario simulation games, virtual reality and mobile games, theme games, board games, and escape room games. The objectives of the studies were diverse. Three studies conducted randomized controlled trials, with only 1 performing a comparative analysis between different games. Two studies carried out long-term outcome evaluations. Conclusions: This scoping review explored 7 types of games for disaster education and provided evidence for future education and training. Further research is needed to establish a long-term evaluation mechanism and a better game-based teaching program to provide more insights into the future of disaster education.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| 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) | 0.004 | 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".