The Advanced Care Research Centre (ACRC) is a new, multi-disciplinary, £20M research centre at the University of Edinburgh. The ACRC will lead society’s response to the grand challenge of an ageing population that is growing in size, longevity and needs through the pursuit of research intended to deliver “high‐quality data‐driven, personalised and affordable care to support the independence, dignity and quality‐of‐life of people living in their own homes and in supported care environments”.
This project sits within the ACRC Academy , a dedicated Centre for Doctoral Training, co-located with the ACRC, whose students will deliver key aspects of the ACRC research agenda through a new doctoral-level research and training programme that will also equip them for careers across a wide range of pioneering and influential leadership roles in the public, private and third sectors.
The PhD with Integrated Study in Advanced Care is a novel, structured, thematic, cohort-based, programme of 48 months duration. Each PhD research project within the Academy has been devised by a supervisory team comprising academic staff from at least two of the three colleges within the University of Edinburgh. Each annual cohort of around twelve will include students with disciplinary backgrounds spanning from engineering and data science to humanities, social science, business and commerce, social work, medicine and related health and care professions. This unique level of diversity is a key attribute of our programme.
Project
Aim
The project will combine quantitative and qualitative data within a Bayesian modelling framework to better understand and ultimately predict how neighbourhood characteristics influence health outcomes and associated care demands in later life.
Objectives
- To identify communities in Scotland and the NE of England with health outcomes among older residents that are concordant or in conflict with model predictions based on neighbourhood characteristics (deprivation, green space, neighbourhood trust/attachment).
- To conduct theoretically-informed (Law, 2004; Low-Choy et al, 2016; Rowse, 2009) qualitative research (focus groups and non-participant observation) with residents of the selected communities, to explore local intersectionalities that might explain concordance with or deviance from statistical models.
To develop an integrative Bayesian approach combining qualitative and quantitative data on neighbourhoods using survey data (such as the English Longitudinal Study of Ageing or the Lothian Birth Cohort Study) to improve models of health outcomes, policy predictions, and resource planning for social care in later life.
Description
Using statistics on mortality and census health indicators, this study will first identify neighbourhoods where older residents’ health outcomes (aged 65 and over) concord or conflict with model predictions based on neighbourhood characteristics (deprivation, green space, trust, ability to influence decision-making), or where inequalities are higher or lower than might be expected based on socio-demographic profiles of an area. Then, contextual reasons for concordance or conflict will be qualitatively explored with local older residents. Finally, Bayesian methods and survey data will be used to accommodate quantitative and qualitative insights to improve model estimates of health outcome and associated care needs.
Eligibility
We are specifically looking for applicants who will view their cutting-edge PhD research project in the context of the overall vision of the ACRC, who are keen to contribute to tackling a societal grand challenge and who can add unique value to – and derive great benefit from – training in a cohort comprising colleagues with a very diverse range of disciplines and backgrounds. We advise prospective candidates to engage in dialogue with the named project supervisor and/or the Director of the Academy prior to submitting an application.
Recruitment
We are running a rolling recruitment process. We will assess applications on a monthly basis, and will continue to do so until our places are filled. The next deadline is 31 March, which will then be moved to 31 April, then 31 May, if places are remaining.
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