Abstract
Introduction: Distress and anxiety are commonly reported during the Magnetic Resonance Imaging (MRI) experience with prior studies suggesting the pre-MRI period is a time of heightened distress. There is a paucity of literature exploring preprocedural distress and anxiety, in particular qualitative research analysing patient experience. Instagram is rapidly becoming an important social media platform though which to conduct health research. A gradually increasing number of studies have examined social media to gain insight into patient experience within medical radiation science (MRS). This study is considered as the first to explore patient experience of MRI using Instagram as a data source. Methods: This study investigated the patient experience during the pre-MRI period by performing a content analysis on open-source Instagram posts. Ethical approval for the study was sought and approved by the Charles Sturt University, Human Research Ethics Committee. Results: Six themes emerged from the extracted data; Journey to the MRI, Waiting, Anticipating the MRI procedure, Preparing for the MRI procedure, Negative interaction, and Fear of the results. Conclusion: The findings of this study provide novel self-reported and unsolicited insight into the diverse, multifactorial, and often concomitant nature of preprocedural MRI anxiety and distress. Implications for practice: This study adds to a growing body of literature advocating for a compassionate, holistic, and person-centered approach when caring for patients in MRI that also considers their emotional and psychological wellbeing.
Original language | English |
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Pages (from-to) | S68-S73 |
Number of pages | 6 |
Journal | Radiography |
Volume | 29 |
Issue number | Supplement 1 |
Early online date | 08 Feb 2023 |
DOIs | |
Publication status | Published - May 2023 |
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Dive into the research topics of '‘Scanxiety’: Content analysis of pre-MRI patient experience on Instagram'. Together they form a unique fingerprint.Prizes
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Academic Staff Higher Degree by Research Workload Support Scheme
Hewis, J. (Recipient), 2016
Prize: Grant › Successful