Identifying coping strategies used by patients at a transgender health clinic through analysis of free‐text autobiographical narratives

Abstract

Background This paper presents an analysis of 32 narratives written by patients waiting for assessment at a transgender health clinic (THC) in England. Narratives are autobiographical free texts, designed to allow patients to describe in their own words their experiences of their gender identity and/or transition prior to a clinic appointment, as part of the assessment process. Objective Narratives were analysed to identify actions prospective patients had taken to manage their (usually lengthy) waiting times, so that these ‘coping strategies’ could be shared with future patients. Design Corpus linguistic methodology was utilized to identify common patterns across the whole corpus of text‐based data, augmented with more detailed sociolinguistic analysis of individual narratives. Results There are broad commonalities in the way the transition experience is described across the corpus in terms of presentation of key experiences and feelings. There are specific descriptions of a number of recurring coping strategies, both positive and negative. Conclusion The empowerment value of writing these narratives may be limited; the existence of recurring key features suggests that patients may feel they have to present their experiences in certain ways to be accepted for treatment. However, dissemination of some positive coping strategies may help future clients of THCs to better cope with waiting times, as well as assisting practitioners in THCs in supporting their patients during this wait. Patient/Public Contribution The clinic's Service Users’ Research Advisory Group contributed to formulating the objective and design of the study. Results were presented at the clinic's annual PPI conference.

Corpus-Assisted Discourse Studies

“…• Type 1: a structured top-down process whereby the researcher applies an a priori framework or set of categories to a number of concordance lines (e.g., Culpeper and Gillings (2018) coding for politeness in the BNC1994/ 2014, and Lutzky (2021b) exploring the pragmatic functions of sorry in customer service interactions on Twitter); • Type 2: a structured bottom-up process whereby the researcher assigns categories to the concordance lines, but these come organically from the corpus rather than being imposed on it (e.g., Kopf (2019) exploring the ways that content policies are enforced on Wikipedia, and Zottola et al (2021) identifying coping strategies that patients use in autobiographical narratives while waiting for assessment at a transgender health clinic); • Type 3: an unstructured bottom-up process whereby the researcher eyeballs the concordance lines and lets that qualitative holistic judgement form the basis of analysis (e.g., McEnery, Baker and Dayrell (2019) identifying previously unrecorded droughts in nineteenth-century Britain, and Levon (2016) exploring the extent to which users on a question-and-answer forum use their replies as an opportunity for stance-taking); • Type 4: an unstructured top-down process whereby the researcher identifies concordance lines which match categories proven to be relevant in other datasets (e.g., Archer and Gillings (2020) identifying potential indicators of deception in Shakespeare's plays, and Appleton (2021) exploring how the unification of Germany is discussed in Hansard). Types 1 and 2 both call for the researcher to sift through each and every concordance line within a sample, but they differ with regard to whether categorisation is something that is applied to, or extracted from, the data.…”