The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey
ImportanceEmotional 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 a...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-12-01
|
| Series: | Frontiers in Psychiatry |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1044378/full |
| _version_ | 1797755132609298432 |
|---|---|
| author | Franz F. Belz Kathryn C. Adair Joshua Proulx Allan S. Frankel J. Bryan Sexton |
| author_facet | Franz F. Belz Kathryn C. Adair Joshua Proulx Allan S. Frankel J. Bryan Sexton |
| author_sort | Franz F. Belz |
| collection | DOAJ |
| description | ImportanceEmotional 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.ObjectivesTo examine whether linguistic characteristics of HCW writing associate with prior, current, and future EE.Design, setting, and participantsA 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 a priori hypothesized linguistic categories in written comments. Analysis of covariance (ANCOVA) assessed associations between these categories and past, present, and future HCW EE adjusting for the word count of comments. Comments with <20 words were excluded.Main outcomes and measuresThe 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.ResultsFor 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 (p < 0.001), power (i.e., references relevant to status, dominance, and social hierarchies, e.g., own, order, and allow) words (p < 0.0001), and words overall (p < 0.001) were associated with higher current and future EE. Using positive emotion words (p < 0.001) was associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p < 0.001) 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.ConclusionFive 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. |
| first_indexed | 2024-03-12T17:42:28Z |
| format | Article |
| id | doaj.art-369716a80e3a423f9f4eead3e84b2384 |
| institution | Directory Open Access Journal |
| issn | 1664-0640 |
| language | English |
| last_indexed | 2024-03-12T17:42:28Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj.art-369716a80e3a423f9f4eead3e84b23842023-08-04T02:12:24ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402022-12-011310.3389/fpsyt.2022.10443781044378The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal surveyFranz F. Belz0Kathryn C. Adair1Joshua Proulx2Allan S. Frankel3J. Bryan Sexton4Duke School of Medicine, Duke University, Durham, NC, United StatesDuke Center for Healthcare Safety and Quality, Duke University Health System, Durham, NC, United StatesSafe and Reliable Healthcare, Evergreen, CO, United StatesSafe and Reliable Healthcare, Evergreen, CO, United StatesDuke Center for Healthcare Safety and Quality, Duke University Health System, Durham, NC, United StatesImportanceEmotional 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.ObjectivesTo examine whether linguistic characteristics of HCW writing associate with prior, current, and future EE.Design, setting, and participantsA 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 a priori hypothesized linguistic categories in written comments. Analysis of covariance (ANCOVA) assessed associations between these categories and past, present, and future HCW EE adjusting for the word count of comments. Comments with <20 words were excluded.Main outcomes and measuresThe 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.ResultsFor 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 (p < 0.001), power (i.e., references relevant to status, dominance, and social hierarchies, e.g., own, order, and allow) words (p < 0.0001), and words overall (p < 0.001) were associated with higher current and future EE. Using positive emotion words (p < 0.001) was associated with lower EE in 2019 (but not 2020). Contrary to hypotheses, using more first person singular (p < 0.001) 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.ConclusionFive 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.https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1044378/fullburnoutemotional exhaustionstresswell-beingLIWClinguistic analyses |
| spellingShingle | Franz F. Belz Kathryn C. Adair Joshua Proulx Allan S. Frankel J. Bryan Sexton The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey Frontiers in Psychiatry burnout emotional exhaustion stress well-being LIWC linguistic analyses |
| title | The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey |
| title_full | The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey |
| title_fullStr | The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey |
| title_full_unstemmed | The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey |
| title_short | The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey |
| title_sort | language of healthcare worker emotional exhaustion a linguistic analysis of longitudinal survey |
| topic | burnout emotional exhaustion stress well-being LIWC linguistic analyses |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1044378/full |
| work_keys_str_mv | AT franzfbelz thelanguageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT kathryncadair thelanguageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT joshuaproulx thelanguageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT allansfrankel thelanguageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT jbryansexton thelanguageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT franzfbelz languageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT kathryncadair languageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT joshuaproulx languageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT allansfrankel languageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey AT jbryansexton languageofhealthcareworkeremotionalexhaustionalinguisticanalysisoflongitudinalsurvey |