The evolution of health and social care

BACKGROUND

We are seeking a prospective DPhil candidate interested in studying the evolution of health and social care research over the very long run. This will primarily involve large scale scientometric databases (such as Dimensions and OpenAlex), and the candidate will be one of the founding members of the new, forthcoming ‘Evolution of Science’ lab hosted within the Demographic Science Unit.

Specifically, this computationally orientated project will involve building large scale natural language processing (NLP) style models to classify hundreds of millions of individual documents within the scientific record, all based on inputs such as titles and abstracts. These deep learning-based classifiers will assign documents into fields of research (FoR) and help identify which methodologies within the health and social care literature are being utilised differentially. The objective will be to make normative and comparative statements about the state of the health and social care literature, and in the process, answer questions such as but not limited to:

1. Has health and social care research – and the subfields within it – become more or less collaborative over time?
2. Has it become more interdisciplinary or intellectually diverse over time when measured by something like classic Rao Sterling-style metrics?
3. How do health and social care scholars migrate between institutions on an international scale?
4. Which countries are differentially focused on which areas of research? What are the major divergences between the Global North and the Global South?
5. Are gendered differences across scientists more pronounced in some specific subfields than others?

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The successful candidate will be embedded into an active and growing Scientometric team within Population Health which publishes successfully in general interest journals. Specifically, they will gain expertise in Python, Shell scripting, databases, and tools related to the analysis of ‘Big Data’ (e.g. Hadoop, Spark, etc).

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

We will recommend various courses which the successful candidate should audit across the University (mostly in the fields of computer science and informatics) and beyond. They would also potentially have the option to be involved with the Public Knowledge Project (to be discussed), the ESRC Centre for Care, and Reproducible Research Oxford.

PROSPECTIVE  STUDENT

Suitable candidates will have a background in a heavily quantitative or computational discipline, such as data science, computer science, informatics, mathematics, physics, engineering, economics, or statistics. They should already be comfortable with data processing with Python. Experience of medical, health, and care related research would be beneficial.