Tracking Mental Health Risks and Coping Strategies in Healthcare Workers’ Online Conversations Across the COVID -19 Pandemic (2022)

Abstract: The mental health risks of the COVID-19 pandemic are magnified for medical professionals, such as doctors and nurses. To track conversational markers of psychological distress and coping strategies, we analyzed 67.25 million words written by self-identified healthcare workers (N = 5,409; 60.5% nurses, 40.5% physicians) on Reddit beginning in June 2019. Dictionary-based measures revealed increasing emotionality (including more positive and negative emotion and more swearing), social withdrawal (less affiliation and empathy, more “they” pronouns), and self-distancing (fewer “I” pronouns) over time. Several effects were strongest for conversations that were least health-focused and self-relevant, suggesting that long-term changes in social and emotional behavior are general and not limited to personal or work-related experiences. Understanding protective and risky coping strategies used by healthcare workers during the pandemic is fundamental for maintaining mental health among front-line workers during periods of chronic stress, such as the COVID-19 pandemic.

## introduction the covid-19 pandemic has magnified existing mental health disparities globally. relative to people working in other fields, healthcare workers have experienced greater exposure to covid-19 and, consequently, higher risk of death and illness as well as more time spent apart from loved ones during quarantine @xcite ). an estimated 150,000-200,0000 healthcare workers have died globally from covid-19 since the start of the pandemic, with higher rates of infection for nurses, women, and workers involved in covid-19 screening, and higher mortality rates among doctors @xcite . deaths and illnesses among healthcare workers have led to severe understaffing in the hardest-hit areas, causing widespread overwork and burnout in the healthcare field @xcite . healthcare workers experienced higher rates of depression and suicide than many other professions before the pandemic @xcite , and suicidality, depression, and anxiety disorders have increased among healthcare workers in the last 2 years @xcite . beyond pandemic-related social isolation, personal health risks, and overwork, healthcare workers additionally cope with feeling responsible for the deaths and symptoms they witness firsthand in their patients @xcite )-experiences exacerbated early in the pandemic by the fact that healthcare workers were often the only people permitted to be physically present in patients' final hours @xcite . for many, the stress of the pandemic has been aggravated by widespread skepticism of vaccines and the medical system @xcite . others have reported survivors' guilt related to having early vaccine access, feelings of powerlessness with respect to limited covid-19 patient treatment options, and the chronic stress of having insufficient personal protective equipment while working, particularly early in the pandemic @xcite . dealing with chronic stress at the front line of an epidemic or pandemic requires extraordinary coping and emotion regulation skills-and, at the same time, likely compromises the mental health of even the most resilient nurses and doctors. in this project, we followed the linguistic trajectories of healthcare workers' risky and protective coping strategies over the course of the pandemic. the following sections first review past research on risk factors and resilience evident in language use following community traumas. we then describe a longitudinal study tracking social and emotional language used by several thousand self-labeled nurses and doctors on reddit, a popular online social discussion platform, over a baseline period followed by roughly 2 years of the pandemic. analyses focused on main effects over time and moderator models exploring how language trajectories varied as a function of health-relevance, self-relevance, and role (nurse or physician). finally, we explore the ethical, theoretical, and practical implications of the findings for clinical psychology and mental health technology. ## method to obtain the initial sample, we first scraped a large sample of comments and submissions from medical-themed forums, or subreddits (r/medicine, 312,357 posts; r/nurses, 14,927 posts; r/emergencymedicine, 46,019 posts; r/askdocs, 1,617,327 posts; r/studentnurse, 191,525 posts), that appeared to be moderated by healthcare professionals and included "flair" indicating users' real-life qualifications or specializations. initially, 2,182,155 messages posted between october 2018 and january 2021 were scraped using the pushshift api ( https://github.com/pushshift/api ). doctors and nurses were categorized via regular expression searches over the flair text of these messages, searching for commonly used phrases and acronyms used by medical professions (e.g., md, m.d., mbbs for doctors; nurse, pccn, nursing, np, lpn, can, rn, r.n., bsn for nurses). a total of 2,585 doctors and 4,138 nurses were identified. next, we downloaded all available comments and posts from the 6,723 self-labeled doctors or nurses on reddit, totaling over 1.25 million texts, beginning in june 2019. the start date was selected in order to establish baseline norms for the sample, providing roughly 6 months of data from before the virus began spreading globally and 9 months before the who declared a pandemic. texts were concatenated by user and then by month, excluding months containing fewer than 100 words. we also excluded months for which fewer than 50% of the words were recognized by our dictionaries; given that over half of conversational language typically consists of function words ("stop words" such as pronouns and articles), texts containing half or more words that were not captured by our lexica are unlikely to be conversational english. finally, we excluded months in which all punctuation made up 50% or more of the text (indicating, e.g., ascii art). the final dataset included 5,409 unique users (@xmath0,271 or 60.5% nurses; @xmath1,138 or 39.5% medical doctors) and 67,247,147 words (@xmath2,090, @xmath3,355, @xmath4. for mixed-effects regression modeling, we regressed language markers on time (linear, quadratic, and cubic effects), including random slopes for time, nested within authors, and specifying an autocorrelation structure of order 1 (corar1) to account for the non-independence of adjacent (lag-1) months; all models used the nlme package @xcite and were plotted with splot @xcite in r version 4.2.0 (r core team, 2022). to simplify the time variable and make the regression coefficients more interpretable, we transformed months into quarters and then assigned each quarter a sequential number, starting with q3 2019 as sequence 0 and ending with q1 2022 as sequence 11. we then squared and cubed the sequence variable to create the polynomial predictors. all references to statistical significance below use an adjusted p-value threshold rather than the traditional .05 in order to partly account for inflated false discovery rates, or type i errors, due to multiple comparisons. mixed-effects regression models tested effects for 30 language variables, each of which were explored in mixed-effects regression models including six tests (three linear and polynomial effects and three moderators). thus, the corrected α level is .00028 using the bonferroni method, a conservative but intuitive correction that is suitable for exploratory analyses in large samples @xcite . data collection methods and analytic strategies were approved by internal ethical review at receptiviti, inc. and meet federal guidelines for exempt research under the u. s. department of health and human services' (2017) revised common rule. consistent with the reddit api's user agreement, all quantitative data are available online, and we will not profit from the use of these data. deidentified data, the high and low empathy lexica we developed, and r code for downloading individuals' messages can be accessed via the project's open science framework (osf) page. word count, liwc-22 @xcite and a sentiment analysis framework, sallee (syntax-aware lexical emotion engine; adams 2022). liwc is a widely used and well-validated dictionary-based text analysis tool that outputs the percentage of words in a given text that fall into one or more of several dozen grammatical (e.g., pronouns, articles), psychological (e.g., emotions, drives), and topical (e.g., work, health) categories. sallee is dictionary-based as well, providing fine-grained measures of specific emotions (e.g., curiosity, surprise, disgust) and summary affective states (e.g., emotionality, positive emotion) in addition to using syntax-based logic allowing words adjacent to emotion terms (e.g., swear words, negations, and intensifiers) to influence category weights @xcite . empathy lexica. the high and low-empathy lexica were both adapted from the data-driven empathy dictionary developed by @xcite , which was initially trained on a gold-standard empathic reaction corpus @xcite . for the revised dictionaries, we first took words in the highest and lowest-weighted quartiles of sedoc et al.'s (2020) empathy dictionary. we then removed person and place names (e.g., abuja, charles; excepting names used synonymously with low or high empathy, such as bundy and gandhi, respectively), low-frequency misspellings (e.g., entraprenerurship), numerals, and other words that appeared to be highly contextual or time-specific. removal judgments were made by the authors, with disagreements resolved through discussion. wildcards were added sparingly to capture additional word variants where it was safe to do so (e.g., ambulance*), and missing british english spellings (e.g., analyse) were added. finally, we separated content and function words in order to explore whether effects were robust across both types of words. the final revised dictionaries included 4,059 words (2,105 low empathy, 1,954 high empathy). changes to the original empathy dictionary (sedoc et al., 2020) were not intended not to improve measurement accuracy; rather, we aimed to increase interpretability and generalizability, with the long-term goal of making the lexica accessible to clinicians and mental health care providers. as in the original dictionary, high empathy words in the revised lexica focused primarily on suffering (e.g., ravaged, hurt, lost) using expressive (e.g., emotions, feel), prosocial language (e.g., provide, reunite), whereas low empathy language included unemotional or technical words (e.g., acknowledge, result) and disagreeable or insensitive language (e.g., idgaf, trashy). for examples used in the current sample, see ## results regression results were consistent with our hypotheses with a few notable exceptions. both effect sizes and aic comparisons indicated that cubic models were the best fits for the data, and quadratic models were always a better fit than linear models, based on the ∆@xmath6. the standard pattern was an approximately flat line at baseline followed by relatively sharp changes over the first year of the pandemic followed by another plateau or period of more gradual change in the same direction (see for the social language categories, doctors and nurses both showed increasing rates of lowempathy words (a pattern that was strongest for less self-referential language; see figure 1 ), swearing (figure 2 ), and social detachment (more "they" pronouns, figure 3 ) over the course of the pandemic. in parallel, healthcare workers showed decreasing rates of words reflecting or referring to social harmony and social engagement (high-empathy, prosocial, affiliation, and question marks) over time. contrary to our predictions, first-person singular pronouns (e.g., i, me, my) decreased over the first year of the pandemic and then plateaued at a relatively low level (figure 4 ). nurses in particular used markedly less "i" (5.2% to 4.2%) from baseline to early 2022. doctors' first-person singular usage was lower than nurses' at baseline (3.9%), perhaps reflecting physicians' relatively higher status @xcite . the emotional language results were partly consistent with our predictions. as expected, emotionality and some negative emotions (namely disgust) increased over time. however, most negative emotion categories did not change significantly over time (e.g., sadness, fear). more surprisingly, overall positive emotional language increased over time, with amusement and admiration showing the strongest effects for specific emotions (figure 5 ). amusement is a low-frequency category (@xmath7.71, @xmath8.62; 56.6% of months had 0% amusement) but showed robust quadratic and cubic effects. results for words referring to politeness and conflict from liwc-22 were nonsignificant, despite showing the predicted trends (increasing conflict and decreasing politeness over time), both @xmath9.10, @xmath10. those categories' low base rates (@xmath11.32% and 0.24%, respectively) may have limited our ability to detect subtle shifts over time. ## discussion the online conversational language of doctors and nurses over the course of the pandemic shows a coherent picture of people coping with chronic stress by self-distancing (fewer first-person singular pro-nouns) and adopting a more socially detached perspective (less empathic and affiliative language). at the same time, healthcare workers did not seem to be eschewing emotions; rather, emotional language increased over time, including more references to disgust and positive emotions in general. the emotional effects should be qualified by the standard caveats of any language-based sentiment analysis: affective words, when categorized correctly, indicate that a person is attending to and talking about an emotion-which sometimes but not always correlates with their emotional state at the time of speaking or writing @xcite . thus, increases in positive emotional language may reflect emotion regulation attempts or coping strategies more than improvements in well-being or mood. what is most striking is not that positive emotional language increased near the end of our sample-which could be explained by decreasing case rates and a slow return of pre-pandemic freedom in much of the worldbut that positive emotionality only dropped notably during the first month of the pandemic and did not decrease again during later spikes in global case or mortality rates (figure 6 ). indeed, post hoc analyses show that positive emotional language correlated weakly with global new case rates per million, @xmath12.015. that pattern may support the supposition that positive language shifts reflected coping strategies (such as positive reframing) rather than overall well-being @xcite . first-person singular pronouns (e.g., i, me, my) decreased across the pandemic, a pattern that was starker for nurses than for doctors. "i"-words typically indicate vulnerability to psychological distress (i.e., neuroticism or trait negative affectivity; tackman et al. 2019) and mental health concerns related to affect dysregulation, including depression @xcite , anxiety @xcite , eating disorders @xcite , and suicidality @xcite . although the results were inconsistent with general psychological distress, a pattern of decreasing first-person singular pronoun usage is consistent with using self-distancing as a self-regulation strategy during periods of chronic stress. people tend to naturally decrease "i" pronouns as a means of distancing themselves from distress and downregulating negative emotions @xcite . coupled with less empathetic language over time, however, decreased "i" rates may represent an adaptation to chronic stress that helps preserve mental stability in the moment but leads to stress dysregulation and interpersonal problems in the future, after the period of severe stress has passed @xcite .