Introduction: Universities offering accredited medical imaging degrees must ensure their graduates can deliver radiographic services including computed tomography (CT). On-campus high-fidelity simulation can potentially facilitate this learning outside the clinical environment yet there is a paucity of research validating its benefits in relation to diagnostic CT. Methods: A pragmatic multiple methods approach tested for differences in knowledge acquired from two high-fidelity CT simulation environments and explored student perceptions of the learning activities. Third year radiography students (n = 62) were randomly assigned to two groups prior to undertaking a CT placement. Group 1 completed learning activities on a remote-access CT scanner (RA) with peer-assisted learning (PAL). Group 2 completed identical tasks on a local-access CT scanner (LA) facilitated by a CT radiographer. RA students were offered additional scan time if so inclined. Students' CT knowledge was assessed pre- and post-clinical placement. Students were surveyed about their learning experiences. Assessment data was analysed via an ANOVA and survey data via descriptive statistics, t-test and thematic analysis. Results: Assessment results demonstrated no significant difference in CT knowledge between the groups (F(1,60) = 0.3, p = 0.6). There was significant improvement in assessment scores between the pre- and post-clinical period for both groups (F(1,60) = 37.4, p < 0.001). Four themes emerged: remote versus local-access capabilities, facilitation versus PAL, use of a real scanner, and preparedness for the learning activity. Conclusion: CT knowledge acquisition via RA with PAL is comparable to LA with facilitation. Students reported increased satisfaction and confidence in CT skills via facilitated LA compared to RA students with PAL. Implications for practice: Opportunity for CT knowledge acquisition is now available outside of the clinical centre via remote-access. PAL requires in-depth training of the peers in technologically rich learning environments.
- Computed tomography
- Peer-assisted learning