Healthcare Workers Mental Health Index
Receptiviti's Healthcare Workers Mental Health Index measures the daily emotional and mental health of 6,500 doctors and nurses. The index scrapes daily comments posted to Reddit by thousands of self-identified doctors and nurses and analyzes their language using Receptiviti's Mental Health API to understand their emotions, evaluate their mental wellbeing, and gauge their capacity for empathy.
In July 2022, a paper was published about this dataset: Tracking Mental Health Risks and Coping Strategies in Healthcare Workers’ Online Conversations Across the COVID-19 Pandemic. Read it here:
Questions and media inquiries? email us healthcarestudy@receptiviti.com
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Our science is extensively validated and has been used in thousands of mental health and therapy-related research studies
Receptiviti's Mental Health Insights identify many of the language-based psychological indicators associated with mental health and distress-related concerns such as depression, stress, and social anxiety. Our models have been constructed and extensively validated by panels of psychologists and have been cited in over 19,000 research studies, of which over 6,500 focus on understanding the linguistic fingerprints of mental health, coping, disease, recovery, and treatment delivery.
Here's a small sample of the research:
Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19
The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world's largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non-mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. By using a broad set of NLP techniques and analyzing a baseline of pre-pandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.
Objective Analysis of Language Use in Cognitive Behavioral Therapy
Substance use disorders commonly co-occur with PTSD symptoms, and the comorbidity is prevalent and difficult-to-treat. We conducted a secondary analysis of patient language use during cognitive-behavioral therapy (CBT) in a randomized clinical trial, comparing a novel, integrated CBT for PTSD/SUD with standard CBT for SUD. We analyzed transcripts of a single, matched session across both treatment conditions using LIWC, and compared to standard CBT for SUD. Patients in the novel, integrated CBT for PTSD/SUD used more negative emotion words but less positive emotion words. Further, exploratory analyses indicated an association between usage of cognitive processing words and clinician-observed reduction in PTSD symptoms, regardless of treatment condition. Our results suggest that language use during therapy may provide a window into mechanisms active in therapy.
Empathy in Motivational Interviewing Includes Language Style Synchrony Between Therapist and Client
Empathy is a basic psychological process that involves the development of synchrony in dyads. It is also a foundational ingredient in specific, evidence-based behavioural treatments like motivational interviewing (MI). To explore a new objective indicator of empathy, we hypothesized that synchrony in language style between client and therapists would predict gestalt ratings of empathy over and above the contribution of reflections. High empathy sessions showed greater synchronicity across 11 language style categories compared to low empathy sessions (p < .01), and overall, average synchronicity was notably higher in high empathy vs. low empathy sessions. These findings suggest empathy ratings are related to synchrony in language style, over and above synchrony of content as measured by therapist reflections.
Behavioral Activation and Depression Symptomatology: Longitudinal Assessment of Linguistic Indicators in Text-Based Therapy Sessions
Behavioral activation is rooted in the behavioral theory of depression, which states that increased exposure to meaningful, rewarding activities is a critical factor in the treatment of depression. Assessing constructs relevant to BA currently requires the administration of standardized instruments, such as the Behavioral Activation for Depression Scale (BADS), which places a burden on patients and providers, among other potential limitations. Previous work has shown that depressed and nondepressed individuals may use language differently and that automated tools can detect these differences. The increasing use of online, chat-based mental health counseling presents an unparalleled resource for automated longitudinal linguistic analysis of patients with depression, with the potential to illuminate the role of reward exposure in recovery. This work investigated how LIWC-based indicators of planning and participation in enjoyable activities identified in online, text-based counseling sessions relate to depression symptomatology over time. LIWC markers of depression and our novel linguistic indicators of activation were strongly associated with depression scores (Patient Health Questionnaire [PHQ]-9) and longitudinal patient trajectories.
Linguistic Analysis of Schizophrenia in Reddit Posts
Linguistic abnormalities are a hallmark of Schizophrenia. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study Schizophrenia's linguistic characteristics. We explore linguistic indicators of schizophrenia Reddit discussion forums. Identifying and detecting signs of SZ is difficult given that SZ is relatively uncommon, affecting approximately 1% of the US population, and people suffering with SZ often believe that they do not have the disorder. Linguistic abnormalities are a hallmark of SZ and many of the illness’s symptoms are manifested through language. In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ. We collected and analyzed a large corpus of Reddit posts from users claiming to have received a formal diagnosis of SZ and identified several linguistic features that differentiated these users from a control (CTL) group. We compared these results to other findings on social media linguistic analysis and SZ. We also developed a machine learning classifier to automatically identify self-identified users with SZ on Reddit.
You Sound So Down: Capturing Depressed Affect Through Depressed Language
We present a new composite indicator of depressed language and examine its utility to capture depressed affect among three nonclinical samples. Depressed individuals use more first person singular pronouns, more negatively valenced words, and fewer positively valenced words. Building on this previous research, we hypothesize that individuals under conditions of chronic or acute stress, a psychological state likely to evoke depressed affect, will evidence increased use of depressed language when compared with those not under tress. Across three studies examining different populations (university faculty, undergraduate college women, and gay men) using multiple markers of stress (perceived stress, stress appraisals, blood pressure), we find that depressed language is consistently positively associated with both acute and chronic stress. Our findings suggest that this measure of depressed language may serve as a useful tool for identifying depressed affect for both practitioners and researchers.
Read more of the research
Click here to see some of the 6,800 mental health studies that use our science.
We invented the science of language psychology
30-years ago we invented the science of language and personality, called LIWC, and we've been perfecting it ever since. Our science is validated, has over 19,000 citations on Google Scholar, and we are continually expanding its capabilities through our collaborative relationships with some of the most acclaimed researchers in the domain from the University of Toronto, the University of Texas, University of Arizona.
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