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Association of Health Care Work With Anxiety and Depression During the COVID-19 Pandemic: Structural Topic Modeling
Matteo Malgaroli1, Emily Tseng2, Thomas D Hull3
1Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY, United States.
JMIR AI
|June 14, 2024
Summary
Health care workers experienced unique work-related stressors during COVID-19, leading to anxiety and depression. Natural language processing identified key mental health challenges in this population.
Area of Science:
- Mental Health
- Public Health
- Computational Linguistics
Background:
- Healthcare workers (HCWs) faced significant stressors during the COVID-19 pandemic, resulting in high rates of depression and anxiety.
- Identifying specific factors contributing to HCWs' psychological distress is crucial for effective mental health interventions, both currently and for future crises.
Purpose of the Study:
- To analyze deidentified psychotherapy transcripts from telemedicine treatments of HCWs during the initial COVID-19 wave in the US.
- To identify specific concerns in HCW treatment and compare them with matched non-HCW patients using natural language processing (NLP).
Main Methods:
- A case-control study involving 820 HCWs and 820 matched non-HCW controls who received digital psychotherapy in Spring 2020.
- Utilized Structural Topic Models (STMs) on deidentified transcripts to identify treatment topics and their association with anxiety and depression.
- Measured depression using Patient Health Questionnaire-9 and anxiety using General Anxiety Disorder-7.
Main Results:
- STM analysis revealed distinct healthcare and mental health topics for HCWs, and pandemic disruptions and mental health topics for controls.
- Specific topics like working on the hospital unit, mood disturbances, and sleep disturbances were significantly associated with moderate to severe anxiety and depression in HCWs.
- No significant associations were found between pandemic-related topics and mental health outcomes for non-HCW controls.
Conclusions:
- The study provides quantitative evidence that HCWs encountered unique work-related stressors during the COVID-19 pandemic, contributing to anxiety and depression.
- Dedicated treatment efforts are necessary to address the mental health needs of HCWs.
- NLP methods can effectively identify clinically relevant distress markers while maintaining patient privacy.
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