The current study aimed to guide the assessment and improvement of psychological safety (PS) by ( 1) examining the psychometric properties of a brief novel PS scale, (2) assessing relationships between PS and other safety culture domains, (3) exploring whether PS differs by healthcare worker demographic factors, and (4) exploring whether PS differs by participation in 2 institutional programs, which encourage PS and speaking-up with patient safety concerns (i.e., Safety WalkRounds and Positive Leadership WalkRounds).Methods: Of 13,040 eligible healthcare workers across a large academic health system, 10,627 (response rate, 81%) completed the 6-item PS scale, demographics, safety culture scales, and questions on exposure to institutional initiatives. Psychometric analyses, correlations, analyses of variance, and t tests were used to test the properties of the PS scale and how it differs by demographic factors and exposure to PS-enhancing initiatives. Results:The PS scale exhibited strong psychometric properties, and a 1-factor model fit the data well (Cronbach α = 0.80; root mean square error approximation = 0.08; Confirmatory Fit Index = 0.97; Tucker-Lewis Fit Index = 0.95). Psychological Safety scores differed significantly by role, shift, shift length, and years in specialty. The PS scale correlated significantly and in expected directions with safety culture scales. The PS score was significantly higher in work settings with higher rates of exposure to Safety WalkRounds or Positive Leadership WalkRounds. Conclusions:The PS scale is brief, diagnostic, and actionable. It exhibits strong psychometric properties; is associated with better safety, teamwork climate, and well-being; differs by demographic factors; and is significantly higher for those who have been exposed to PS-enhancing initiatives.
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.
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