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Funding providers: ESRC and Health & Care Research Wales (HCRW)
Subject areas: Computer Science (Artificial intelligence) / Health Informatics / Health and Social Care / Child development
Project start dates:
- 1 October 2023 (Enrolment open from mid-September)
Supervisors:
- Professor Sue Jordan
- Dr Ben Mora
Aligned programme of study: PhD in Medical and Health Care Studies
Mode of study: Full or part-time study is possible.
Project description:
Context
Education outcomes predict life chances, but vary with socioeconomic status. However, disability may also be a barrier to optimal school performance. Disability and learning difficulties may emanate from congenital anomalies (CA), affecting ~3% of children in Wales: CA and other long-term childhood conditions are more prevalent in economically-deprived communities (Spencer et al., 2015). Most (87-97%) European children with congenital anomalies now survive infancy. In Wales, 97.8% (97.5-98.1) of children alive at 28 days survive to 5 years (Santoro et al., 2022).
Rationale
Now that medical needs of children with CA are largely met, attention must be drawn to wider aspects of their wellbeing, and life chances. However, in Wales (Rawlings et al., 2022), physical disability is not the only factor affecting educational outcomes.
Aims
This study aims to identify the magnitude of combined effects of deprivation, congenital anomalies, ethnicity, looked-after status, care received, and other factors, to inform targeting of support.
Questions
- Which factors or patterns of factors predict poor school performance?
- Where and how should services be targeted to overcome disadvantage, prevent deterioration (e.g. children with congenital heart defects), strengthen families, and protect human rights to education (protocol 1 article 2).
Provisional scope
We shall explore associations between combinations of factors affecting educational outcomes at key stages 2 and 4 (ages 11 and 16), and any deterioration. AI is the optimum strategy to identify impacts of the full range of exposures and social factors, from conception into childhood. We shall include all children in Wales born after 1.1.1998, surviving to age 11.
Design
We shall develop a specific AI model based on neural networks (Kapcia et al., 2021, Eshkiki et al., 2021). This “brain” will then learn patterns automatically from the dataset (Nagoor et al., 2022), as extracted from SAIL.
Methods
We shall use existing SAIL data on education outcomes and SEN provision, deprivation (all components of the Welsh index of multiple deprivation (WIMD)), free school meals, sex, ethnicity (including travellers), provision of services (and social services and local authority care records), health visitor assessments, congenital anomalies, perinatal outcomes, disability, medicines prescribed to mothers and children, all diagnoses (including COVID-19 and mental health conditions), primary care contacts, substance misuse, breastfeeding, vaccines, hospital admissions, distance from sources of environmental pollution (e.g. landfill), and, for a subset, genetic data. Maternal sibships and schools attended will be accounted. Some of these linkages have been tested in European projects (Loane et al., 2021).
Eligibility
ESRC studentships are highly competitive. Candidates should have an excellent background in the social sciences, holding a first- or strong upper-second-class degree, or equivalent recognised by Swansea University (please consult https://www.swansea.ac.uk/postgraduate/apply/entry-requirements/); applications from those also holding a relevant research training master’s degree (or an equivalent background in research training) will be considered for a ‘+3’ /PhD award. Graduates from Heath Data Science, Psychology, Computer Science or related disciplines, with knowledge of or willingness to learn about social sciences, are encouraged to apply. Training in statistics can be provided.
English language requirements: if applicable, a minimum overall IELTS score of 6.5 (with a score of no less than 6.5 in any individual component) or Swansea University-recognised equivalent is required.
A fully-funded Wales DTP studentship is available to both UK and international (including EU and EEA) students. All applicants will be eligible for a full award consisting of a maintenance stipend and payment of tuition fees at the UK research organisation rate. Applicants must satisfy studentship eligibility requirements. For further details, please see the UKRI website. Successful international student applicants will receive a fully-funded Wales DTP studentship and will not be charged the fees difference between the UK and international rate.
Pending qualification study: to meet Swansea University's concurrent study regulations, current study is required to be awarded/completed (as appropriate) prior to the course/scholarship start date. Please note that this date supersedes any current course of study schedule/submission deadlines.
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