Lessons From Low- and Middle-Income Countries: Alleviating the Behavioral Health Workforce Shortage in the United States

The United States is facing a mental health workforce shortage, exacerbated by the COVID-19 pandemic. Low- and middle-income countries (LMICs) have historically grappled with even greater shortages. Therefore, LMICs have thought creatively about expanding the mental health workforce and the settings in which to deliver evidence-based and equitable mental health care. The authors introduce some mental health interventions in LMICs, describe evidence of the efficacy of these interventions gleaned from this context, and discuss the applicability of these interventions to the United States. The authors also reflect on the benefits and challenges of implementing these interventions in the U.S. mental health care system to alleviate its current workforce shortage.

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

[This corrects the article DOI: 10.3389/fpsyt.2022.1044378.].

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.