http://theses.ncl.ac.uk/jspui/handle/10443/5255 2026-06-10T20:19:58Z 2026-06-10T20:19:58Z Alderson, Ashleigh http://theses.ncl.ac.uk/jspui/handle/10443/6808 2026-06-10T14:52:45Z 2025-01-01T00:00:00Z

Title: An action research approach to relationships and sex education (RSE) in the digital era Authors: Alderson, Ashleigh Abstract: Young people increasingly rely on digital technologies to communicate with peers, and explore their sexuality, much to the concern of adults. However, adults’ anxieties over young peoples’ technology use often results from morals and technopanics, failing to consider the opportunities that technology can offer. Morals and technopanics often translates into education, with Relationships and Sex Education (RSE) focussing on harms and at times even victim blaming young people for harms experienced through technology. This research utilised an Action Research (AR) methodology to understand, and respond to, the impact of digital technology, namely TikTok, on relationships among young people. The first AR cycle involved semi-structured interviews with 12 young people. Findings include opportunities that TikTok offers to marginalised young people including LGBTQ+ communities, and potential harms including addiction and what I termed ‘algorithmic segregation’ whereby those who do not fit the platforms’ ideals gain less visibility as content creators. In response to these findings, I developed an algorithmic awareness intervention designed for the RSE curriculum. Feedback on the intervention was sought through an online survey involving RSE teachers. The survey’s findings revealed that there is no ‘one size fits all’ as each school delivers RSE differently. The intervention could not be employed within schools due to the Covid pandemic. It was instead employed within a youth group context in a deprived area of the North East of England as the final AR cycle. The findings of this AR cycle highlight the need for tailoring inclusive learning resources to the diverse needs of learners specifically accounting for the needs of SEN learners and boys who are often forgotten within RSE, which typically focuses on, and at times pathologises, girls and their behaviour. I discuss the findings from this research and provide recommendations for policy, education, healthcare, and future research. Description: PhD Thesis

2025-01-01T00:00:00Z Wilson, Sarah http://theses.ncl.ac.uk/jspui/handle/10443/6798 2026-05-22T14:26:55Z 2025-01-01T00:00:00Z

Title: Strategies to promote digital health equity Authors: Wilson, Sarah Abstract: Digital Health Technologies (DHTs) have revolutionised healthcare, but their benefits are not equally experienced among under-served populations. This PhD project aimed to identify those most at risk of digital exclusion within healthcare and explore strategies to promote inclusivity. First, the researcher reviewed the literature to identify sociodemographic factors that may contribute to digital exclusion within healthcare, organising them into six groups to form the CLEARS framework (Culture, Limiting conditions, Education, Age, Residence, and Socioeconomic status), which recognises intersectionality across these groups (Chapter 3). This review also highlighted a knowledge gap around the needs and experiences of under-served groups, and the strategies that might support their digital inclusion. Inspired by this, the researcher conducted a systematic review of the literature to identify strategies to promote digital health equity (Chapter 4). The review highlighted the importance of user-friendly designs, supportive infrastructure (e.g., free devices and connectivity), and digital skills educational support. A qualitative study, using semi-structured interviews and focus groups, was conducted with 29 under-served individuals who represented the CLEARS groups to explore their perspectives of these strategies (Chapters 5-6). Participants raised concerns regarding the use of their social network for digital skill support (e.g., experiencing controlling behaviours) and highlighted the need to increase funding for educational support services. Co-design approaches were also suggested to ensure DHTs were designed appropriately and tailored to meet users’ needs. To understand whether these digital inclusion strategies are feasible at a local or regional level, the researcher conducted a second qualitative study with 17 stakeholders who had a professional interest in making decisions and/or delivering activities to support under-served groups at risk of digital exclusion (Chapter 7). Stakeholders emphasised the need for cross-organisational collaboration to implement free devices and connectivity, which were resource intensive. They also stressed the need for staff training to upskill healthcare professionals and develop a knowledge base of local digital inclusion support that under-served groups can be referred to. Based on all the findings from this PhD programme of work, the researcher created eight key recommendations to advance digital inclusion within healthcare, including co-designing DHTs with user involvement, raising awareness of available support amounts under-served communities, and providing various digital inclusion support services (e.g., educational digital skills support, re-purpose devices and pre-paid SIM cards). Further research should assess the feasibility and impact of these recommendations in practice. Description: PhD Thesis

2025-01-01T00:00:00Z Iqbal, Anum http://theses.ncl.ac.uk/jspui/handle/10443/6791 2026-05-22T10:47:25Z 2025-01-01T00:00:00Z

Title: Socioeconomic inequalities in polypharmacy amongst older people Authors: Iqbal, Anum Abstract: The use of multiple medications, polypharmacy, is common amongst older people and is an increasing public health challenge. The prevalence of polypharmacy across different populations has been well characterised, but what is less clear is how polypharmacy varies according to socioeconomic position. This thesis aims to address this evidence gap by exploring if there are social inequalities in polypharmacy, amongst older people. First a systematic review and meta-analysis were undertaken to synthesise international evidence on social inequalities and polypharmacy. The evidence showed people with low socioeconomic status had greater likelihood of being in receipt of polypharmacy. Education was the most commonly used socioeconomic measure, and individuals with lower levels of education had a 21% increased likelihood of polypharmacy. Second, data from longitudinal cohort studies, the two Cognitive Function and Ageing Studies (CFAS), were used to explore how socioeconomic status (measured by years of education) was associated with polypharmacy and how relationships have changed over time. Data from CFAS I (from 1991-1993) and CFAS II (2008-2009) were used in the analyses. Baseline waves of both CFAS I and CFAS II were compared. The analysis highlighted the sustained high level of polypharmacy, and multivariable logistic regression showed the widening inequalities in polypharmacy over the 20-year time period. Results from this chapter therefore informed the final analyses, to understand how inequalities in polypharmacy change across different medication groups. These analyses demonstrated that inequalities persist across different medication groups, although for some such effects (such as medications belonging to the cardiovascular group) were more pronounced than others. The work of this thesis has highlighted inequalities in polypharmacy that are widening over time. This has happened at a time when healthcare is more sophisticated and expenditure has risen, but arguably insufficient importance has been placed on prevention and public health. People working in policy and practice should consider how to address social inequalities in polypharmacy alongside current policy initiatives (e.g. the use of Structured Medication Reviews). Description: Ph. D. Thesis.

2025-01-01T00:00:00Z Hassan, Neha Abdelkhale Mohamed http://theses.ncl.ac.uk/jspui/handle/10443/6788 2026-05-19T14:42:20Z 2024-01-01T00:00:00Z

Title: Reducing post-operative infections : the development and validation of artificial intelligence-predictive model to inform shared decision making Authors: Hassan, Neha Abdelkhale Mohamed Abstract: Background: Healthcare systems worldwide generate sizeable patient-related health data. There is growing interest amongst clinicians and healthcare staff in how this can be used to support patient care. One example is the use of predictive analytics in determining the risk of developing a particular complication, such as an infection post-surgery. Objective: To develop an artificial intelligence (AI) model to predict the likelihood of post-operative infection in surgical patients, while also exploring clinicians’ and patients’ perceptions on using AI decision support tools more broadly to inform shared decision making. Methods: This PhD programme of work involved a number of different stages. The literature was systematically reviewed for AI models that could inform clinical decision making with regard to post-surgical infection, and a candidate list of positive predictor variables extracted. A prognostic AI-model was developed to predict the risk of infection, and any inherent biases identified. Another systematic review was conducted to understand how clinicians and patients perceive using AI decision aids in shared decision making, and semi-structured interviews were carried out with clinicians to explore how to improve the clinical utility of AI decision support tools. Results: Nine steps were identified for developing AI-predictive models; the first six steps were applied in the development and evaluation of our model. Nineteen predictors were used. The ensemble model displayed high performance in training (sensitivity: 85.3%, specificity: 74.6%, AUC: 88.6%) and internal validation (sensitivity: 96.9%, specificity: 74.1%, AUC: 85.5%). Patients and clinicians raised concerns about AI model interfaces, in general, and their impact on clinical/patient conversations. Several suggestions were made on how to improve the model’s clinical application. Conclusion: This study provided a deeper understanding of how AI-predictive models can guide shared decision making. Future work should concentrate on improving the user inclusivity of these tools and reducing the risk of inherent bias that could potentially mislead clinical decision making. Description: PhD Thesis

2024-01-01T00:00:00Z