Computed tomography learning via high-fidelity simulation for undergraduate radiography students

Area of Science:

• Medical Imaging Education
• Radiographic Technology Training
• Diagnostic CT Simulation

Background:

• Accredited medical imaging programs must equip graduates with computed tomography (CT) skills.
• High-fidelity simulation offers a potential avenue for CT training outside clinical settings.
• Research validating simulation's effectiveness for diagnostic CT learning is limited.

Purpose of the Study:

• To compare CT knowledge acquisition between remote-access (RA) and local-access (LA) high-fidelity simulation environments.
• To explore radiography students' perceptions of learning via RA with peer-assisted learning (PAL) versus LA with radiographer facilitation.
• To assess the impact of simulation type on student confidence and satisfaction in CT skills.

Main Methods:

• A pragmatic, multiple-methods approach was employed with 62 third-year radiography students.
• Students were randomized into two groups: RA with PAL or LA with radiographer facilitation.
• CT knowledge was assessed pre- and post-clinical placement; student experiences were surveyed.

Main Results:

• No significant difference in CT knowledge was found between the RA and LA simulation groups.
• Both groups showed significant improvement in CT knowledge from pre- to post-placement.
• Four themes emerged: access capabilities, facilitation methods, scanner use, and preparedness.

Conclusions:

• Remote-access CT simulation with peer-assisted learning is as effective for knowledge acquisition as local-access simulation with facilitation.
• Students reported greater satisfaction and confidence with local-access, facilitated simulation.
• Remote-access simulation expands CT training opportunities beyond clinical sites, but peer-assisted learning requires robust training.