The risk of burnout in academic radiographers during the COVID-19 pandemic

Abstract

INTRODUCTION: The COVID-19 pandemic created major challenges across society, healthcare provision and also for those delivering healthcare education programmes. Clinical placements were disrupted and, in many incidences cancelled. Higher education institutions were required to move completely to online delivery methods with little notice. This created significant additional workload, stress and the need to learn new skills at a time of great uncertainty. This study explored the risks of burnout in academic radiographers during the first 12 months of the pandemic. METHODS: A survey was circulated using SurveyMonkey™ via personal, national and international networks, including the European Federation of Radiographer Societies (EFRS), to reach as many academic radiographers as possible. Disengagement and exhaustion were measured using the Oldenburg burnout inventory. Descriptive statistics and a one-way ANOVA were used to analyse the quantitative data using STATA V16 (Statacorp, TA). RESULTS: 533 academic radiographers responded to the survey from 43 different countries. Mean disengagement was in the medium range and exhaustion was high for the total dataset. In a subset of countries with 10 or more responses, there was significant variation between countries, with the UK having highest mean exhaustion score and the UK, Ireland and France sharing the highest mean disengagement score. In the total dataset, 86% agreed workload had increased during the pandemic and 35% had considered leaving academia in the last year. CONCLUSION: These data demonstrate the stark reality of the impact of the COVID-19 pandemic on academic radiographers' workload, wellbeing, and intention to leave their roles. IMPLICATIONS FOR PRACTICE: COVID-19 has had a significant impact on academic radiographers and this study highlights the urgent need for remedial measures to better support academic radiographers in order to ensure a sufficient, and sustainable workforce.