A wellbeing podcast for diagnostic radiography students.

Introduction: Diagnostic radiography students experience challenges during clinical placements, which have the potential to impact students' emotional wellbeing. This study aimed to explore radiography students' perception of the newly developed podcast series as a wellbeing support tool.

Methods: A mixed methods study was conducted analysing data from listeners, including usage data from the podcast host site, surveys, and focus groups.

Framework for a radiography student podcast.

Podcasts refer to episodes of audio content that are readily available on streaming applications on smartphones or computers. This paper reports on the development of the 'Breathe In Radiography Podcast' series for radiography students and provides suggestions for evaluation. Podcast development followed a structured framework, including identification of podcast topics and expert guests, content development, audio recording, episode upload to host site and distribution.

Teaching evidence-based practice: Case study of an integrated assessment task for diagnostic radiography students.

Introduction: A core competency for all health care professionals is evidence-based practice (EBP). An understanding of research skills are key to diagnostic radiographers adopting EBP, and should be taught and assessed in curricula leading to eligibility to register and practice. This paper focuses on the design, implementation and initial evaluation of an assessment task in the Diagnostic Radiography (DR) curriculum at an Australian university, which aimed to facilitate students' skills to identify and interpret research methods and output as a foundation for EBP by combining with EBP and DR theoretical content.

Using Occlusion-Based Saliency Maps to Explain an Artificial Intelligence Tool in Lung Cancer Screening: Agreement Between Radiologists, Labels, and Visual Prompts.

Occlusion-based saliency maps (OBSMs) are one of the approaches for interpreting decision-making process of an artificial intelligence (AI) system. This study explores the agreement among text responses from a cohort of radiologists to describe diagnostically relevant areas on low-dose CT (LDCT) images. It also explores if radiologists' descriptions of cases misclassified by the AI provide a rationale for ruling out the AI's output.

Understanding radiographic decision-making when imaging obese patients: A Think-Aloud study.

Introduction: The incidence of obesity has been steadily rising over the last few decades and is having a significant impact upon the health system. In radiography, a particular challenge of imaging obese patients is implementing the as low as reasonably achievable (ALARA) principle when determining radiation dose, and technical and patient-care adaptations. This study aimed to better understand the decision-making strategies of experienced radiographers in determining imaging and exposure factor selection in the context of imaging obese patients.

A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.

Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group.

Optimizing Projectional Radiographic Imaging of the Abdomen of Obese Patients: An e-Delphi Study.

Purpose: Obesity is increasing in prevalence globally, with increased demands placed on radiology departments to image obese patients to assist with diagnosis and management. The aim of this study was to determine perceived best practice techniques currently used in clinical practice for projectional radiography of the abdomen for obese patients with the aim to help elucidate areas for future research and education needs in this field.

Experimental Design: A two round e-Delphi study was undertaken to establish a consensus within a reference group of expert Australian clinical educator diagnostic radiographers (CEDRs).

Implementing a working together model for Aboriginal patients with acute coronary syndrome: an Aboriginal Hospital Liaison Officer and a specialist cardiac nurse working together to improve hospital care.

Acute coronary syndrome (ACS) contributes to the disparity in life expectancy between Aboriginal and non-Aboriginal Australians. Improving hospital care for Aboriginal patients has been identified as a means of addressing this disparity. This project developed and implemented a working together model of care, comprising an Aboriginal Hospital Liaison Officer and a specialist cardiac nurse, providing care coordination specifically directed at improving attendance at cardiac rehabilitation services for Aboriginal Australians in a large metropolitan hospital in Melbourne.