Anxiety, depression, and stress among radiography undergraduates during the COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic has severely impacted education and other aspects of life, causing psychological distress. The current study aims to identify anxiety, depression, and stress among radiography undergraduates during the COVID-19 pandemic. METHOD: A descriptive, cross-sectional study was conducted between November and December 2021 on a sample of 140 radiography undergraduates at the Department of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya. An online survey with two sections: demographic characteristics and a psychometric scale (DASS-42) was used for data collection. RESULTS: A total of 107 undergraduates responded to the questionnaire giving a response rate of 76.2%. The results revealed that the majority of radiography undergraduate students suffered from mild to extremely severe depression (87.85%), anxiety (92.52%), and stress (73.83%) levels. In addition, more than two-thirds of the students (>73% of participants) reported at least one symptom of depression, anxiety, or stress to varying degrees. Scores for depression, anxiety, and stress did not differ significantly across gender and academic year. However, a significant difference was observed between the two age groups, 23-26 years and > 27 years, regarding depression. The older students reported severe depression, whereas younger students reported moderate depression. CONCLUSION: A high prevalence of negative psychological impact was observed among radiography undergraduates during the COVID-19 pandemic. This necessitates taking proactive steps to address, safeguard, and nurture undergraduates' mental health and well-being during the current and future pandemic crises to mitigate the negative impacts.

Radiographers' perceptions of first year diagnostic radiography students' performance following implementation of a simulation-based education model.

INTRODUCTION: Simulation-based education (SBE) partially replaced the clinical placement learning for a cohort of first year students on a BSc (Hons) Diagnostic Radiography programme. This was in response to the pressures on hospital-based training caused by increasing student numbers and following increased capability and positive outcomes for student learning in delivering SBE as a result of the COVID-19 pandemic. METHODS: A survey was distributed to diagnostic radiographers, across five NHS Trusts, involved in the clinical education of first year diagnostic radiography students at one UK university. The survey sought radiographers' perception of student performance in undertaking radiographic examinations, safety procedures, knowledge of anatomy, professionalism, and the impact of embedding simulation-based education through multichoice and free text questions. Descriptive and thematic analysis of the survey data was undertaken. RESULTS: Twelve survey responses from radiographers across four Trusts were collated. Responses indicated the majority of radiographers perceived students to require the expected level of assistance in undertaking appendicular examinations, applying infection control and radiation safety measures, and had the expected level of radiographic anatomy knowledge. Students also interacted appropriately with service users, demonstrated increased confidence in coming into the clinical environment and were receptive to feedback. Some variation was noted, particularly in professionalism and engagement, though not always attributed to SBE. CONCLUSION: Replacement of clinical placement with SBE was perceived to have provided appropriate learning opportunities and some additional benefits, however it was felt by some radiographers that SBE could not replace the experience of the real imaging environment. IMPLICATIONS FOR PRACTICE: Embedding simulated-based education requires a holistic approach and close collaboration with placement partners to ensure complimentary learning experiences in the clinical placement setting, and support achievement of the learning outcomes.

Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey.

BACKGROUND: Artificial Intelligence (AI) technologies have already started impacting clinical practice across various settings worldwide, including the radiography profession. This study is aimed at exploring a world-wide view on AI technologies in relation to knowledge, perceptions, and expectations of radiography professionals. METHODS: An online survey (hosted on Qualtrics) on key AI concepts was open to radiography professionals worldwide (August 1st to December 31st 2020). The survey sought both quantitative and qualitative data on topical issues relating to knowledge, perceptions, and expectations in relation to AI implementation in radiography practice. Data obtained was analysed using the Statistical Package for Social Sciences (SPSS) (v.26) and the six-phase thematic analysis approach. RESULTS: A total of 314 valid responses were obtained with a fair geographical distribution. Of the respondents, 54.1% (157/290) were from North America and were predominantly clinical practicing radiographers (60.5%, 190/314). Our findings broadly relate to different perceived benefits and misgivings/shortcomings of AI implementation in radiography practice. The benefits relate to enhanced workflows and optimised workstreams while the misgivings/shortcomings revolve around de-skilling and impact on patient-centred care due to over-reliance on advanced technology following AI implementation. DISCUSSION: Artificial intelligence is a tool but to operate optimally it requires human input and validation. Radiographers working at the interface between technology and the patient are key stakeholders in AI implementation. Lack of training and of transparency of AI tools create a mixed response of radiographers when they discuss their perceived benefits and challenges. It is also possible that their responses are nuanced by different regional and geographical contexts when it comes to AI deployment. Irrespective of geography, there is still a lot to be done about formalised AI training for radiographers worldwide. This is a vital step to ensure safe and effective AI implementation, adoption, and faster integration into clinical practice by healthcare workers including radiographers. CONCLUSION: Advancement of AI technologies and implementation should be accompanied by proportional training of end-users in radiography and beyond. There are many benefits of AI-enabled radiography workflows and improvement on efficiencies but equally there will be widespread disruption of traditional roles and patient-centred care, which can be managed by a well-educated and well-informed workforce.