Discourse analysisBMJ 2008; 337 doi: http://dx.doi.org/10.1136/bmj.a879 (Published 07 August 2008) Cite this as: BMJ 2008;337:a879
- Brian David Hodges, associate professor, vice chair (education), and director1,
- Ayelet Kuper, assistant professor2,
- Scott Reeves, associate professor3
- 1Department of Psychiatry, Wilson Centre for Research in Education, University of Toronto, 200 Elizabeth Street, Eaton South 1-565, Toronto, ON, Canada M5G 2C4
- 2Department of Medicine, Sunnybrook Health Sciences Centre, and Wilson Centre for Research in Education, University of Toronto, 2075 Bayview Avenue, Room HG 08, Toronto, ON, Canada M4N 3M5
- 3Department of Psychiatry, Li Ka Shing Knowledge Institute, Centre for Faculty Development, and Wilson Centre for Research in Education, University of Toronto, 200 Elizabeth Street, Eaton South 1-565, Toronto, ON, Canada M5G 2C4
- Correspondence to: B D Hodges
Previous articles in this series discussed several methodological approaches used by qualitative researchers in the health professions. This article focuses on discourse analysis. It provides background information for those who will encounter this approach in their reading, rather than instructions for conducting such research.
What is discourse analysis?
Discourse analysis is about studying and analysing the uses of language. Because the term is used in many different ways, we have simplified approaches to discourse analysis into three clusters (table 1⇓) and illustrated how each of these approaches might be used to study a single domain: doctor-patient communication about diabetes management (table 2⇓). Regardless of approach, a vast array of data sources is available to the discourse analyst, including transcripts from interviews, focus groups, samples of conversations, published literature, media, and web based materials.
What is formal linguistic discourse analysis?
The first approach, formal linguistic discourse analysis, involves a structured analysis of text in order to find general underlying rules of linguistic or communicative function behind the text.4 For example, Lacson and colleagues compared human-human and machine-human dialogues in order to study the possibility of using computers to compress human conversations about patients in a dialysis unit into a form that physicians could use to make clinical decisions.5 They transcribed phone conversations between nurses and 25 adult dialysis patients over a three month period and coded all 17 385 words by semantic type (categories of meaning) and structure (for example, sentence length, word position). They presented their work as a “first step towards an automatic analysis of spoken medical dialogue” that would allow physicians to “answer questions …