The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey - Belz, Franz F.; Adair, Kathryn C.; Proulx, Joshua; Frankel, Allan S.; Sexton, J. Bryan (Frontiers in Psychiatry, 2022)
Importance: Emotional exhaustion (EE) rates in healthcare workers (HCWs) have reached alarming levels and been linked to worse quality of care. Prior research has shown linguistic characteristics of writing samples can predict mental health disorders. Understanding whether linguistic characteristics are associated with EE could help identify and predict EE. Objectives: To examine whether HCW EE is associated with linguistic characteristics in written comments. Design, Setting, and Participants: A large hospital system in the Mid-West had 11,336 HCWs complete quality improvement surveys in 2019, and 10,564 HCWs in 2020. Surveys included a measure of EE and an open-ended comment box. Linguistic Inquiry and Word Count (LIWC) software assessed the frequency of linguistic categories in written comments. ANCOVA compared one exploratory and eight a priori hypothesized linguistic categories to HCW EE within and across years controlling for the word count of comments. Comments with <20 words were excluded. Main Outcomes & Measures: The frequency of the nine linguistic LIWC categories in HCW comments were examined across EE quartiles. Results: For the 2019 and 2020 surveys, 2,101 and 1,418 comments (103,474 and 85,335 words) were analyzed respectively. Comments using more negative emotion (p<.001), power words (p<.0001), and words overall (p<.001) were associated with higher current and future EE. Using positive emotion words (p<.001) were associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p<.001) predicted lower current and future EE. Past and present focus, first person plural, and social words did not predict EE. Current EE did not predict future language use. Conclusions: Five linguistic categories predicted current and subsequent HCW EE. Notably, EE did not predict future language. These linguistic markers suggest a language of EE, potentially offering insights into EE’s etiology, consequences, measurement, and intervention.