Effectiveness of a bite-sized web-based intervention to improve healthcare worker wellbeing: A randomized clinical trial of WISER

"WISER" intervention to reduce healthcare worker burnout - 1 year follow up

OBJECTIVE

Test sustainability of Web-based Implementation for the Science of Enhancing Resilience (WISER) intervention efficacy in reducing healthcare worker (HCW) emotional exhaustion (EE), a key component of burnout.

DESIGN

One-year follow-up of WISER RCT using two cohorts (one waitlist control with shortened intervention period) of HCWs of four NICUs each, to improve HCW well-being (primary outcome: EE).

RESULTS

In Cohorts 1 and 2, 194 and 312 WISER initiators were identified by 1-year, and 99 and 80 completed 1-year follow-up, respectively. Combined cohort results showed that relative to baseline, at 1-year WISER decreased EE (-7.07 (95%CI: -10.22, -3.92), p < 0.001), depression (-4.49 (-6.81, -2.16), p = <0.001), and improved work-life integration (6.08 (4.25, 7.90), p = <0.001). EE continued to decline between 6-month and 1-year follow-up (p = 0.022). The percentage of HCWs reporting concerning outcomes was significantly decreased for EE (-10.9% (95%CI: -17.9%, -4.9%); p < 0.001), and secondary outcomes depression and work-life integration.

CONCLUSION

WISER improves HCW well-being for at least 1 year.

CLINICAL TRIALS NUMBER

NCT02603133; https://clinicaltrials.gov/ct2/show/NCT02603133.

Self-compassion letter tool for healthcare worker well-being: a qualitative descriptive analysis

OBJECTIVE

This qualitative study aimed to identify categories within therapeutic self-compassion letters written by healthcare workers. Resulting categories were assessed for their relevance to the construct of self-compassion.

DESIGN

This was a qualitative descriptive study that used summative content analysis and inductive coding.

SETTING

A US-based academic healthcare system.

PARTICIPANTS

Healthcare workers who attended a self-compassion webinar were recruited.

INTERVENTION

The online self-compassion tool asked participants to write a letter to themselves from the perspective of a friend providing support and encouragement.

RESULTS

116 letters were analysed. Five major categories emerged: Looking Forward, Reaffirming Self, Reaffirming Reminders, Hardships and Self-Disparagement. Respondents' letters were mostly positively framed and forward thinking, including their hopes of improving themselves and their lives in the future. Negative content generally described hardships and often served to provide self-validation or perspective on obstacles that had been overcome.

CONCLUSION

The writing prompt elicited content from the writers that reflected the core elements of self-compassion (ie, self-kindness, common humanity, mindfulness). Continued research to further understand, refine and improve the impact of therapeutic letter writing to enhance well-being is warranted to reduce burnout and promote quality patient care.

Stressors Among Healthcare Workers: A Summative Content Analysis

Healthcare workers are experiencing high stress and burnout, at rates up to 70%, hindering patient care. Studies often focus on stressors in a particular setting or within the context of the pandemic which limits understanding of a more comprehensive view of stressors experienced by healthcare workers. The purpose of this study was to assess healthcare workers’ self-reported major stressors. Between June 2018 and April 2019, U.S. healthcare workers ( N = 2,310) wrote answers to an open-ended question: “What are your biggest stressors as you look back over the last few weeks?” A summative content analysis was used to analyze the data. Healthcare workers described three types of stressors: work stressors (49% of total stressors), personal life stressors (32% of total stressors), and stressors that intersect work and personal life (19% of total stressors). Future research and clinical practice should consider the multi-faceted sources of stress.

The language of healthcare worker emotional exhaustion: A linguistic analysis of longitudinal survey

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 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 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 (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.

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.