Corpus data on metaphor in end-of-life care

Semino, Elena (2019). Corpus data on metaphor in end-of-life care. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-851543

The primary aim of this project is to investigate the use of metaphor in the experience of end-of-life care in the UK. A systematic analysis will be conducted of the metaphors used by members of different stakeholder groups (patients, unpaid family carers and healthcare professionals) in a 1.5-million-word corpus consisting of interviews and contributions to online fora.
The secondary aim of this project is to investigate the implications of the use of metaphor in the data for existing descriptions and theories of metaphor as a linguistic and cognitive phenomenon. The method that will be employed to identify and analyse metaphor in the data is both qualitative and quantitative. The most innovative aspect of this method is the exploitation of the semantic annotation tool within the online software tool Wmatrix, which will make it possible to identify metaphorical expressions more systematically than is currently possible with other corpus-based methods for the study of metaphor in large data sets.

The findings of the project will ultimately be relevant to the provision of end-of-life care, and to the consultancy for, and training of, health professionals.

Data description (abstract)

This data collection consists of five different text corpora, i.e. large collections of language data, generated for the systematic analysis of the metaphors used by members of different stakeholder groups such as patients, unpaid family carers and healthcare professionals when talking about end-of-life care. One textual data file consists of transcribed interviews with 16 healthcare professionals. The remaining four consist of data drawn from publicly-accessible online fora: (1) the online forum for cancer patients and carers run by Macmillian Cancer Support; (2) the online forum “doc2doc” for health professionals run by the BMJ Publishing Group; (3) healthcare professionals’ responses to BMJ comment articles; and (4) blog posts on BMJ Blogs.

Data creators: