Fully funded research studentships in the Faculty of Health and Medicine at Lancaster University
ESRC CASE PhD Studentship: Understanding how people living with bipolar disorder talk about risk on social media
Individuals living with bipolar disorder are likely to engage in behaviours which can be risky for themselves or others. This includes increased prevalence of suicide and self‐harm, excessive spending, alcohol or drug use and risky sexual behaviour. Understanding more about this behaviour is crucial as with the right help people living with bipolar “have the potential to return to normal function with optimal treatment”p 45 (NICE, 2014).
Current psychological models of bipolar explain risky behaviour as an attempt to avoid low mood, a response to mood elevation or to impulsivity/sensitivity to reward. These approaches have informed the development of psychological interventions to improve coping strategies for mood change. However, the effectiveness of such approaches is mixed and evidence is lacking for improvements in the functional and recovery outcomes which qualitative research has shown are valued. Current research has relied on questionnaire measures of hypothesised processes, which limits what can be learnt about the subjective experiences of people living with bipolar. For instance, they tell us little about how such individuals define risk, why they chose to engage in some such behaviours and how socially normative such behaviour might be. It is clear therefore, that a mixed method approach is needed to understand the processes which underpin risk in bipolar. This should combine in‐depth qualitative approaches with methods that explore how people describe their experiences in natural language, not constrained by typical research or clinical settings. This is particularly important for risky behaviour that is likely to have been stigmatised. Services users increasingly share information through Facebook, Twitter, Reddit (a comprehensive network of user forums) and blogs. The volume of such data would prevent manual processing but computational linguistics offers opportunities to learn more about how people describe their risk experiences on these platforms. Natural language processing has been employed to predict suicidality. In contrast to this potentially ethically problematic predictive approach, this research seeks to understand what such behaviours mean to the individual, how they calibrate risk, and why they chose to engage or not with risk. This ESRC-funded CASE studentship studentship will take a mixed‐methods approach. The student will conduct a systematic review of risk taking in mood disorders to inform a qualitative investigation of this area in bipolar disorder, to help to shape a framework for the natural language processing of social media posts on Twitter and Reddit. To ensure relevance to people living with bipolar disorder the PhD student will work with service user advisors to finalise the implementation and dissemination of the research. This builds on work by the supervisory team using this approach to understand personal recovery in bipolar disorder. Key questions 1. What does current research tell us about relationships between risky behaviour and the experiences of people living with mood disorders (including bipolar disorder)? 2. How do people with bipolar disorder describe risk? 3. What range of risky behaviours do people with bipolar describe? 4. What reasons do people report for risky behaviour and what contextual and emotional factors influence this?
This full-time three-year CASE Studentship covers fee plus a stipend of £15,009. The studentship is available from October 2020
Apply via email to Professor Steve Jones: firstname.lastname@example.org
Applicants must include:
C.V. (max 2 A4 sides), including details of two academic references, a cover letter outlining their qualifications and interest in the studentship (max 2 A4 sides).
ESRC PhD Studentship: Improving respiratory care via statistical modelling of multiple sources of data
Data science and statistics are rapidly transforming the face of health care and are leading to efficiency increases in prevention, diagnosis and treatment for a multitude of diseases.
This PhD project will address respiratory disease, using as a case study data from the Morecambe Bay area in the Northwest of England, and will aim to use multiple sources of data to identify factors that may be useful to improve the care of people suffering from respiratory disease in deprived communities.
Respiratory disease remains a leading cause of morbidity and mortality in the UK and has strong links with poor housing, deprivation and health inequalities; this is particularly the case in the Morecambe Bay Area, which contains some of the most deprived communities in the country. Nationally, there is currently considerable focus on the importance of robust recognition and diagnosis of respiratory disease in the primary care setting. This is important not only for correct and timely treatment of individual patients but also to reduce the burden on local health services caused by non-elective admissions and lengthy hospital stays. In addition, the national policy requires accurate diagnostic coding to inform strategic health planning for respiratory disease. Tasked with addressing these issues, the Morecambe Bay Respiratory Network (MBRN) is the emerging integrated respiratory service in Bay and Health Care Partners. The three main objectives of the PhD are:
- Develop statistical models for the spatio-temporal epidemiology of respiratory disease in the MBRN patch. Identify the relationships between incidence of respiratory disease, deprivation and housing in our locality; determine the factors affecting space-time changes in patterns of respiratory disease.
- Develop machine learning classification algorithms to understand the features of diagnostic quality for the four main chronic respiratory diseases (COPD, Asthma, Bronchiectasis and ILD). Identify areas of good practice and areas that may require improved training in respiratory care and support from population health strategies.
- Develop statistical models for predicting how well patients control their symptoms and evaluate outcomes 1 year from initial diagnoses. Compare expected to actual outcomes in patients on the MRBN pathway vs control groups in and out of Morecambe Bay, thus evaluating the clinical benefits of MBRN.
The PhD project will be based in the Royal Lancaster Infirmary Business Intelligence/Data Science Unit and at the Data Science Institute at Lancaster University and will form part of the portfolio of research associated with our forthcoming Health Innovation Campus and the NIHR Applied Research Collaboration North West Coast. The supervisory team includes both academic and key clinical partners to ensure the research goals are both intellectually-innovative and of practical relevance and utility to the NHS in our local area and more widely at the national level. The student will receive advanced training at the machine learning / statistics / epidemiology interface.
The is an ESRC studentship which will pay UK/EU tuition fees and a starting stipend of approx £18,000 per annum.
How to Apply
The successful candidate will be highly motivated, capable of independent work, with a first-class Bachelor's degree, or distinction at Master statistics, or a related discipline with substantial statistical content. Applicants must have an interest in Health Data Science, together with good interpersonal and communications skills.
Please include an up-to-date CV. The project will start in October 2020. The first round interviews will be held in late February with second-round interviews in late March if required.
The Lancaster University campus is situated in a beautiful 360 acre parkland site at Bailrigg, just 3 miles from Lancaster City Centre. Lancaster University is one of Britain’s top universities, with over 12,000 students and 2,500 employees within the Bailrigg campus that is now almost a small town in its own right. For those applicants who enjoy the outdoors, living in Lancaster offers easy access to the Lake District and Yorkshire Dales.