Search

Background: Liver disease is an increasing cause of morbidity and mortality in the United Kingdom and can be challenging to live with in the advanced stages. There has been little research exploring the healthcare experiences of UK individuals with decompensated disease when the liver cannot carry out its functions properly. A PhD research project was developed with people who have liver disease to explore care experiences in decompensated advanced liver disease. Public involvement (PI) is an essential aspect of meaningful health research, and this paper reports on the progression of our PI approach in this ongoing study. Objective: To embed PI throughout the research project to ensure that the study is meaningful to individuals with liver disease and the people who support them. Methods: The research adopts a Constructivist Grounded Theory methodology to develop a theory of care experience. Various PI approaches were considered in developing the PI strategy for this qualitative study. Initially, Embedded consultation was the preferred model, which has evolved to include aspects of collaboration and coproduction. A PI group was set up to oversee the project through the national public engagement website VOICE, and reflections on PI from three members of the group are included in this paper to illuminate the PI process. Results: Six individuals with liver disease and three carers from across the United Kingdom are part of an ongoing PI group. Their role includes commenting on the findings of the systematic literature review for this project and contributing to decisions about recruitment, data collection and data analysis. Additionally, they had a direct impact on changing the focus of the research. The PI group will continue involvement until the completion of the project. Conclusion: Successfully embedding PI into doctoral research, as demonstrated in this project, requires commitment, planning and dedication to reciprocal working for the benefit of PI contributors as well as the research. This approach could be adopted by other postgraduate researchers. Patient or Public Contribution: This project is overseen by the PI group, whose contribution is described throughout, including reflections from three PI group members. [ABSTRACT FROM AUTHOR]

Background: Healthcare workers (HCWs) in the United Kingdom (UK) have faced many challenges during the COVID-19 pandemic, some of these arising out of their social positions. Existing literature explicating these challenges (e.g., lack of appropriate PPE, redeployment, understaffing) have highlighted inequities in how these have been experienced by HCWs based on ethnicity, gender or, job role. In this paper, we move a step ahead and examine how the intersection of these social positions have impacted HCWs' experiences of challenges during the pandemic. Methods: We collected qualitative data, using interviews and focus groups, from 164 HCWs from different ethnicities, gender, job roles, migration statuses, and regions in the United Kingdom (UK) between December 2020 and July 2021. Interviews and focus groups were conducted online or by telephone, and recorded with participants' permission. Recordings were transcribed and a hybrid thematic analytical approach integrating inductive data-driven codes with deductive ones informed by an intersectional framework was adopted to analyse the transcripts. Results: Thematic analysis of transcripts identified disempowerment, disadvantage and, discrimination as the three main themes around which HCWs' experiences of challenges were centred, based on their intersecting identities (e.g., ethnicity gender, and/or migration status). Our analysis also acknowledges that disadvantages faced by HCWs were linked to systemic and structural factors at the micro, meso and macro ecosystemic levels. This merging of analysis which is grounded in intersectionality and considers the ecosystemic levels has been termed as 'intrasectionalism'. Discussion: Our research demonstrates how an intrasectional lens can help better understand how different forms of mutually reinforcing inequities exist at all levels within the healthcare workforce and how these impact HCWs from certain backgrounds who face greater disadvantage, discrimination and disempowerment, particularly during times of crisis like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Introduction: Social Prescribing has an established recognition regarding the benefits provided to the health-related social needs of adults, but little is known about how the intervention addresses young people's needs. There is optimism regarding the central role of two core mechanisms that allows social prescribing to be effective, such as the empathetic role of Link Workers and the connection with community resources. This paper aims to describe the role played by Link Workers working a Social Prescribing intervention targeting young people. Description: This paper adopts a case study methodology to describe the role of Link Workers addressing young people's needs and implementing Social Prescribing scheme in Sheffield (UK). Data were collected through semi-structured interviews with four of the seven link workers of one organisation based in Sheffield. Data were analysed through an inductive approach for emerging themes. Discussion: We provided a description of the profiles and background of Link Workers and described the three models of referral pathways into the intervention. The paper also shows how Link Workers identify young people's needs and how they connect with the community. Conclusion: Based on the insights and the internationally accepted definition of Social Prescribing, we provide a visual representation of the Social Prescribing model and discuss challenges. The paper highlights lessons learned and future directions regarding the role of Link Workers from the case study. [ABSTRACT FROM AUTHOR]

Simple Summary: According to cancer research statistics, there are 7500 new ovarian cancer diagnoses in the UK each year. An earlier detection of ovarian cancer leads to a better prognosis; however, there is currently no screening programme for ovarian cancer, and detection using ultrasound examinations remains challenging. The use of artificial intelligence in gynaecological ultrasound examinations aims to improve the diagnostic accuracy of ultrasound for ovarian cancer and improve outcomes for patients. This review aims to collate current research on AI in the ultrasound diagnosis of ovarian cancer and suggests the usefulness of incorporating this into clinical care. Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80–0.82), and specificity was 92% (95% CI, 0.92–0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice. [ABSTRACT FROM AUTHOR]