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The Serious Game and Integrated Simulator for Cardiopulmonary Resuscitation Training in Nursing Students

Author

Listed:
  • Ayla Demirtas
  • Tulay Basak
  • Gul Sahin
  • Murat Çagatay Sonkaya

Abstract

Background Nurses are often the first health professional who identify cardiopulmonary arrests in hospital settings; therefore, they require proficient cardiopulmonary resuscitation skills. Aim The purpose of this study was to evaluate game-based cardiopulmonary resuscitation training for first-year nursing students in terms of students’ knowledge levels, performance, satisfaction, and confidence. Methods This study was conducted following a quasi-experimental design. A total of 104 students were randomly assigned to two study groups. A Serious Game & Integrated Real-Time Audiovisual Feedback Simulator was utilized in the cardiopulmonary resuscitation training of the intervention group. For the control group, only a real-time audiovisual feedback simulator was used. Results There was an increase of approximately 30 points between the pre-test and post-test scores for knowledge in both groups. In regard to performance scores, there were no statistically significant differences between the groups in regard to overall scores, compression depth, and compression frequency were similar at post-test and re-test (p>0.05). The difference between groups was not statistically significant for satisfaction and self-confidence statistically (p>0.05). Conclusion The integration of a serious game into a real-time audiovisual feedback simulator does not provide significant advantages over the use of only a real-time audiovisual feedback simulator in terms of cardiopulmonary resuscitation knowledge and performance.

Suggested Citation

  • Ayla Demirtas & Tulay Basak & Gul Sahin & Murat Çagatay Sonkaya, 2022. "The Serious Game and Integrated Simulator for Cardiopulmonary Resuscitation Training in Nursing Students," Simulation & Gaming, , vol. 53(2), pages 97-110, April.
  • Handle: RePEc:sae:simgam:v:53:y:2022:i:2:p:97-110
    DOI: 10.1177/10468781211073162
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    Cited by:

    1. Minh Chau & Elio Stefan Arruzza, 2023. "Maximising Undergraduate Medical Radiation Students’ Learning Experiences Using Cloud-Based Computed Tomography (CT) Software," Simulation & Gaming, , vol. 54(4), pages 447-460, August.

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