ArticleFrontiers in psychiatry2022
The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey.
Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Is the patient speaking or the nurse? Automatic speaker type identification in patient-nurse audio recordings.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- ADscreen: A speech processing-based screening system for automatic identification of patients with Alzheimer's disease and related dementia.Artificial intelligence in medicine · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
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 linguistic characteristics of HCW writing associate with prior, current, and future EE. Design setting and participants: A large hospital system in the Mid-West had 11,336 HCWs complete annual quality improvement surveys in 2019, and 10,564 HCWs in 2020. Surveys included a measure of EE, an open-ended comment box, and an anonymous identifier enabling HCW responses to be linked across years. Linguistic Inquiry and Word Count (LIWC) software assessed the frequency of one exploratory and eight Main outcomes and measures: The frequency of the linguistic categories (word count, first person singular, first person plural, present focus, past focus, positive emotion, negative emotion, social, power) in HCW comments were examined across EE quartiles. Results: For the 2019 and 2020 surveys, respondents wrote 3,529 and 3,246 comments, respectively, of which 2,101 and 1,418 comments (103,474 and 85,335 words) contained ≥20 words. Comments using more negative emotion ( Conclusion: Five linguistic categories predicted current and subsequent HCW EE. Notably, EE did not predict future language. These linguistic markers suggest a language of EE, offering insights into EE's etiology, consequences, measurement, and intervention. Future use of these findings could include the ability to identify and support individuals and units at high risk of EE based on their linguistic characteristics.
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