Game-based vs. Case-based Training for Increasing Knowledge and Behavioral Fluency of Nurse Students Regarding Crisis and Disaster Management; a Quasi-Experimental Study.
Bibliographic record
Abstract
Introduction: Nurses play an active role in disaster response, and the ability of nurses to appropriately apply management principles during large-scale disasters or mass casualty incidents is of critical importance. This study aimed to compare the effect of game-based Training (GBT) and case-based training (CBT) on nursing students' knowledge and behavioral fluency regarding Crisis and Disaster Management. Methods: This is a quasi-experimental study with a pretest-posttest design. Convenience sampling was used to select third-year nursing students who had completed their clinical clerkship at the time of the study (n=60). In the intervention group, disaster-themed games were used, while in the control group, CBT was used. The emergency and crisis management course consisted of this study's theoretical and clinical training phases. After completion of the theoretical phase (five weeks), the practical part (four weeks) is completed as an internship. The data was collected from the disaster Nurses' Knowledge Questionnaire, demographic survey, and measurement checklists for disasters and crises at five stations. Results: GBT students achieved significantly higher knowledge scores than CBT students after training (p< 0.001). CBT and GBT groups had no significant differences in Objective Structured Clinical Examination (OSCE)1 pretest scores. Posttest1-OSCE2 and posttest2-OSCE3 scores showed significant differences after one week (P < 0.001) and one month (P < 0.001). The mean pretest and posttest1 scores were statistically significant in both groups. A comparison of posttest scores between one month after GBT training (69.03 ± 6.09) and one week after it (69.23 ± 6.14) revealed no statistical significance (p = 0.056). Conclusion: Nursing students' knowledge and behavioral fluency regarding crisis management were more effectively improved by using the disaster and crisis game than by using a case-based method.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".