Introduction: In 2019 the World Health Organisation published a report which concluded microplastics in drinking water did not present a threat to human health. Since this time a plethora of research has emerged demonstrating the presence of plastic in various organ systems and their deleterious pathophysiological effects. Methods: A scoping review was undertaken in line with recommendations from the Johanna Briggs Institute. Five databases (PubMed, SCOPUS, CINAHL, Web of Science and EMBASE) were systematically searched in addition to a further grey literature search. Results: Eighteen articles were identified, six of which investigated and characterised the presence of microplastics and nanoplastics (MNPs) in the human urinary tract. Microplastics were found to be present in kidney, urine and bladder cancer samples. Twelve articles investigated the effect of MNPs on human cell lines associated with the human urinary tract. These articles suggest MNPs have a cytotoxic effect, increase inflammation, decrease cell viability and alter mitogen-activated protein kinases (MAPK) signalling pathways. Conclusion: Given the reported presence MNPs in human tissues and organs, these plastics may have potential health implications in bladder disease and dysfunction. As a result, institutions such as the World Health Organisation need to urgently re-evaluate their position on the threat of microplastics to public health. Impact statement: This scoping review highlights the rapidly emerging threat of microplastic contamination within the human urinary tract, challenging the World Health Organisation’s assertion that microplastics pose no risk to public health. The documented cytotoxic effects of microplastics, alongside their ability to induce inflammation, reduce cell viability and disrupt signalling pathways, raise significant public health concerns relating to bladder cancer, chronic kidney disease, chronic urinary tract infections and incontinence. As a result, this study emphasises the pressing need for further research and policy development to address the challenges surrounding microplastic contamination.

Understanding the patient experience of admission to a psychiatric mother-baby unit (MBU) informs service improvement and strengthens patient-centered care. This study aims to examine patients’ experience, satisfaction, and change in mental health status related to MBU admission. At discharge, 70 women admitted to a public MBU completed the Patient Outcome and Experience Measure (POEM), rated the usefulness of therapeutic groups, and provided written qualitative feedback. Paired sample t-tests, correlations, and thematic content analysis were completed. Women were highly satisfied with the level of care and support received, particularly for those who were voluntarily admitted. Women reported an improvement in mental health from admission to discharge. Women appreciated the staff’s interpersonal skills, provision of practical skills, education, advice, support from other women, and therapeutic groups offered. Women suggested improvements such as having greater food choices, more MBU beds, more group sessions, family visitations, which had been restricted due to COVID-19, environmental modifications, and clarity of communication surrounding discharge. This study highlights the benefits of MBUs and the specific aspects of care that are favorable in treating women with mental illnesses who are co-admitted with their baby in an MBU.

Care plans outline collaborative goals and strategies for recovery. While care planning is recommended across international mental health guidelines, scant attention has examined the unique nature ...

Purpose: The COVID-19 pandemic had a profound negative effect on mental health worldwide. The hospital emergency department plays a pivotal role in responding to mental health crises. Understanding data trends relating to hospital emergency department usage is beneficial for service planning, particularly around preparing for future pandemics. Machine learning has been used to mine large volumes of unstructured data to extract meaningful data in relation to mental health presentations. This study aims to analyse trends in five mental health-related presentations to an emergency department before and during, the COVID-19 pandemic. Methods: Data from 690,514 presentations to two Australian, public hospital emergency departments between April 2019 to February 2022 were assessed. A machine learning-based framework, Mining Emergency Department Records, Evolutionary Algorithm Data Search (MEDREADS), was used to identify suicidality, psychosis, mania, eating disorder, and substance use. Results: While the mental health-related presentations to the emergency department increased during the COVID-19 pandemic compared to pre-pandemic levels, the proportion of mental health presentations relative to the total emergency department presentations decreased. Several troughs in presentation frequency were identified across the pandemic period, which occurred consistently during the public health lockdown and restriction periods. Conclusion: This study implemented novel machine learning techniques to analyse mental health presentations to an emergency department during the COVID-19 pandemic. Results inform understanding of the use of emergency mental health services during the pandemic, and highlight opportunities to further investigate patterns in presentation.

Purpose: Sustainable leadership is essential for addressing workforce shortages, technological advancements, and increasing regulatory demands in Australian healthcare. Many healthcare leaders assume their roles based on clinical expertise rather than formal leadership training, highlighting the need for structured support. This study explores sustainable leadership in Australian healthcare, identifying key challenges, support mechanisms, and strategies for improvement. Methods: A cross-sectional survey was conducted among 276 managers, leaders, and supervisors working in Australian healthcare organisations. Participants were recruited through professional networks, social media, and direct invitations. The survey, administered via Qualtrics, examined leadership training, characteristics of sustainable leadership, challenges, and available support systems. Quantitative data were analysed using IBM SPSS Statistics, while qualitative responses underwent thematic analysis. Results: Leadership training was primarily informal, with limited access to structured programs due to time and financial constraints. Sustainable leadership was defined as balancing operational demands with long-term planning, ethical decision-making, and fostering a resilient workplace culture. Key challenges included staff retention, change management, and hierarchical structures limiting innovation. Support for leaders was inconsistent, with male leaders reporting higher perceived support. Systemic barriers, such as outdated leadership models and a focus on financial performance over workplace culture, restricted sustainable leadership implementation. Conclusion: To enhance sustainable leadership, organisations must prioritize structured training, mentorship, and inclusive leadership pathways. Addressing systemic barriers and redefining leadership success beyond financial metrics will strengthen leadership resilience, reduce burnout, and improve healthcare outcomes.

Machine learning (ML) and artificial intelligence are increasingly ubiquitous in healthcare data analytics. To date, however, ML has been largely restricted to the analysis of structured data. While natural language processing (NLP) is gaining prominence in healthcare, substantial challenges remain in the generation and analysis of unstructured data. Emergency departments, which are increasingly under-resourced and overburdened, may benefit from the implementation of ML techniques that incorporate NLP to support clinical decision-making and improve patient care. Historically, regional and cultural variations have posed significant challenges to the widespread application of ML algorithms beyond their original training datasets. The rapidly increasing use of NLP within clinical note-taking applications provides avenues to assist in standardizing unstructured data and extracting meaningful insights to improve generalization and clinical translation.