Maximising Undergraduate Medical Radiation Students’ Learning Experiences Using Cloud-Based Computed Tomography (CT) Software

Minh Chau and Elio Stefan Arruzza

Simulation & Gaming, 2023, vol. 54, issue 4, 447-460

Abstract: Background Simulation-based learning is a crucial educational tool for disciplines involving work-integrated learning and clinical practice. Though its uptake is becoming increasingly common in a range of fields, this uptake is less profound in diagnostic radiography and computed tomography. Aim This study explored whether CT simulator software may be a viable option to facilitate the development of practical clinical skills in an effective, safe and supported environment. Methods A cross-sectional mixed methods design was employed. Students in their third year of study undertook formal simulation CT learning using the Siemens SmartSimulator, prior to a six-week off-campus clinical experience. A pre- (n = 42, response rate = 39%) and post-clinical placement Likert scale survey was completed (n = 21, retention rate = 50%), as well as focus group interviews to gather qualitative data (n = 21). Thematic analysis was employed to explore how the simulator developed students’ knowledge of CT concepts and preparedness for clinical placement. Results Survey scores were high, particularly in terms of satisfaction and relevancy. Focus groups drew attention to the software’s capacity to build on foundational principles, prepare students for placement and closely emulate the clinical environment. Students highlighted the need for continual guidance and clinical relevance and maintained that interactive simulation was inferior to real-world clinical placement. Conclusion The integration of CT simulator software has the potential to increase knowledge, confidence, and student preparation for the clinical environment.

Keywords: simulation; computed tomography; radiography; undergraduate students; curriculum (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:simgam:v:54:y:2023:i:4:p:447-460

DOI: 10.1177/10468781231178491

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