Thematic analysis and natural language processing of job‐related problems prior to physician suicide in 2003–2018

Abstract

Introduction:Although previous studies have consistently demonstrated that physicians are more likely than non-physicians to experience work-related stressors prior to suicide, the specific nature of these stressors remains unknown. The current study aimed to better characterize job-related problems prior to physician suicide. Methods:The study utilized a mixed methods approach combining thematic analysis and natural language processing to develop themes representing death investigation narratives of 200 physician suicides with implicated job problems in the National Violent Death Reporting System database between 2003 and 2018.Results: Through thematic analysis, six overarching themes were identified: incapacity to work due to deterioration of physical health, substance use jeopardizing employment, interaction between mental health and work-related issues, relationship conflict affecting work, legal problems leading to work-related stress, and increased financial stress. Natural language processing analysis confirmed five of these themes and elucidated important subthemes.Conclusions: This is the first known study that integrated thematic analysis and natural language processing to characterize work-related stressors preceding physician suicide. The findings highlight the importance of bolstering systemic support for physicians experiencing job problems associated with their physical and mental health, substance use, relationships, legal matters, and finances in suicide prevention efforts.

Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

“…Furthermore, Twitter is an ideal microblogging social media site for analysis of naturally occurring data to examine how suicide is talked about during COVID-19 pandemic, as users can upload messages of up to 280 characters of text, also referred to as “tweets”. Previous studies have found natural language processing techniques and such social media analyses to be a feasible and novel method to study public sentiment and emotional manifestations occurred on a given topic [ 15 , 16 , 17 , 18 ].…”

Relationship between duration of sick leave and time variation of words used in return-to-work programs for depression

“…For example, a study investigating work-related tweets using Twitter reported that tweets containing words such as “life” and “need” were included regardless of the emotion expressed in the tweet [ 25 ]. Another study that used NLP to investigate work-related suicides in the National Violent Death Reporting System database identified factors such as mental health issues and conflicts in relationships as preceding factors of suicide [ 26 ].…”

Institutional suicide as anomie: decedents speak out for work-related suicides through a Durkheimian exploration of suicide notes in a context without institutional responsibilization for suicide prevention

“…For example, while only 2.1% of suicides among those working in protective services were listed on the death certificate as work-related suicide, the cause of death was cited in 15.2% of death investigations of the same group, indicating that work-related suicides were much higher than documented ( Peek-Asa et al, 2021b ). In another study of physician suicides in the USA between 2013 and 2018, examined by integrating natural language processing and thematic analysis, five themes were identified in work-related physician suicides: inability to work due to poor physical health, substance use that compromises employment, the interaction between mental health and work-related issues, legal issues leading to work-related stress, and increased financial stress ( Kim et al, 2022 ). Work-related suicides in the United States increased by 22.2% between 1995 and 2010, becoming one of the country’s leading causes of death.…”