Person-first and Identity-first language: a text-mining exploration of how geneticists discuss autism

Introduction: Current discussions surround whether ’person-first language’ (PFL) such as ’patient with autism’ and ’identity-first language’ (IFL) such as ’autistic patient’ is most sensitive and appropriate. There is language guidance when talking about disability (1; 2) and race, ethnicity, and ancestry in genetics research (3), but not around PFL and IFL. We applied natural language processing (NLP) methods to PFL and IFL in published in genetics research, focussing on Autism Spectrum Disorders (ASD). 

Methods: Of the approximately 38,000 abstracts accepted in European Society
of Human Genetics (ESHG) conference between 2001 and 2021, almost 5,000 contained autism keywords. NLP analysis of these explored PFL and IFL use over time, in combination with specific nouns, and in combination with each other.

Results: 262 instances of PFL and 264 instances of IFL showed similar, common and consistent use over time. Straightforward matches (e.g. ’patient with ASD’ or ’ASD patient’) accounted for most uses, with subtle differences in the frequently co-occurring nouns. 50 abstracts used both patterns, typically with one example of each.

Conclusions: NLP can quantify use, timing and context for PFL and IFL in research articles. Consequently, NLP can support the development of language style guidelines or to evaluate their effectiveness.