Following the online trail of veterans and improving clinical PTSD therapy

En la primera parte de esta memoria analizamos r/veterans de Reddit usando LDA para extraer los temas discutidos y analizar el contenido de las publicaciones. En la segunda parte exploramos los problemas con la terapia para PTSD y proponemos un sistema para mejorar las sesiones de terapia grupal

Peer Support to Reduce Vicarious Trauma in Mental Health Providers

Mental health providers working with individuals with trauma and stress are at increased risk for vicarious trauma (VT) and compassion fatigue. A quality improvement project was implemented with six mental health providers engaged in weekly peer support group sessions lead by the DNP student over a six week timeframe. Topics discussed included education on vicarious trauma, client case vignettes, and mindfulness around the treatment of vicarious trauma. Professional quality of life (ProQOL) and secondary traumatic stress (STSS) assessments were completed prior to the start of the peer group and at the completion of the sixth group. Analysis of post group assessment results demonstrated a strong agreement by 67% that peer support is necessary. Additional findings from the peer support groups concluded that connection and the sense of feeling less alone was beneficial, resulting in more clinic camaraderie and individual confidence with patient care. The mental health providers also felt supported by their leadership as a result of their participation. The results of this quality improvement project demonstrate that peer support groups may reduce provider burnout and vicarious trauma, and increase compassion satisfaction

SMHD : a large-scale resource for exploring online language usage for multiple mental health conditions

Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users’ language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language

SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions

Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.Comment: COLING 201

Automatic extraction of informal topics from online suicidal ideation

Abstract Background Suicide is an alarming public health problem accounting for a considerable number of deaths each year worldwide. Many more individuals contemplate suicide. Understanding the attributes, characteristics, and exposures correlated with suicide remains an urgent and significant problem. As social networking sites have become more common, users have adopted these sites to talk about intensely personal topics, among them their thoughts about suicide. Such data has previously been evaluated by analyzing the language features of social media posts and using factors derived by domain experts to identify at-risk users. Results In this work, we automatically extract informal latent recurring topics of suicidal ideation found in social media posts. Our evaluation demonstrates that we are able to automatically reproduce many of the expertly determined risk factors for suicide. Moreover, we identify many informal latent topics related to suicide ideation such as concerns over health, work, self-image, and financial issues. Conclusions These informal topics topics can be more specific or more general. Some of our topics express meaningful ideas not contained in the risk factors and some risk factors do not have complimentary latent topics. In short, our analysis of the latent topics extracted from social media containing suicidal ideations suggests that users of these systems express ideas that are complementary to the topics defined by experts but differ in their scope, focus, and precision of language.https://deepblue.lib.umich.edu/bitstream/2027.42/144214/1/12859_2018_Article_2197.pd

Detecting Mental Distresses Using Social Behavior Analysis in the Context of COVID-19: A Survey

Online social media provides a channel for monitoring people\u27s social behaviors from which to infer and detect their mental distresses. During the COVID-19 pandemic, online social networks were increasingly used to express opinions, views, and moods due to the restrictions on physical activities and in-person meetings, leading to a significant amount of diverse user-generated social media content. This offers a unique opportunity to examine how COVID-19 changed global behaviors regarding its ramifications on mental well-being. In this article, we surveyed the literature on social media analysis for the detection of mental distress, with a special emphasis on the studies published since the COVID-19 outbreak. We analyze relevant research and its characteristics and propose new approaches to organizing the large amount of studies arising from this emerging research area, thus drawing new views, insights, and knowledge for interested communities. Specifically, we first classify the studies in terms of feature extraction types, language usage patterns, aesthetic preferences, and online behaviors. We then explored various methods (including machine learning and deep learning techniques) for detecting mental health problems. Building upon the in-depth review, we present our findings and discuss future research directions and niche areas in detecting mental health problems using social media data. We also elaborate on the challenges of this fast-growing research area, such as technical issues in deploying such systems at scale as well as privacy and ethical concerns

Articulating the new normal(s) : mental disability, medical discourse, and rhetorical action.

“Articulating the New Normal(s): Mental Disability, Medical Discourse, and Rhetorical Action” studies the writing of people diagnosed with autism and post- traumatic stress disorder within online discussion boards related to mental health and outlines their unique rhetorical strategies for interacting with biomedical ideologies of psychiatry and activist discourses. The opening chapter situates this dissertation in relation to previous scholarship in Rhetoric, Disability Studies, and other fields. I also provide a summary of the set of mixed methods I use to gather and analyze my data, including rhetorical analysis, corpus analysis, and qualitative interviews. In Chapter 2, “Medical Terminology and Discourse Features of Online Discussions of Mental Health,” I explore the ways in which medical discourse appears in discussions of mental disability through medical terms that writers and speakers use when discussing a diagnosis. Using methods borrowed from linguistics, I demonstrate that the writers in my study make different linguistic choices than the general public, and that the most prominent differences are related to the social construction of mental health and medicine. In Chapter 3, “Inhabiting Biological Primacy with Chiasmic Rhetoric in Mental Health Forums,” I describe and analyze a variety of common topics in online conversations that connect mental health and expert knowledge of the brain. I argue that this connection of mental experience and brain science constitutes a chiasmic rhetoric. The writers foregrounded in this chapter acknowledge and accept much of the claims of medicine and neuroscience regarding the brain but, uniquely, work to divide that knowledge from the path of normativity and optimization. Chapter 4, “Classified Conversations: Psychiatry and Technical Communication in Online Spaces,” examines the practices of participants in online mental health discussion forums conversations as they interpret technical documents. I detail four salient forms of the manipulation of medical discourse in online communities. At the close of this chapter, I explain how these insights can inform academic study of writing in mental health contexts and transform the content and application of medical and technical texts. In Chapter 5, “Re-Forming Mental Health: Rhetorical Innovation and the Language of Advocacy,” I summarize and synthesize the core arguments of earlier chapters, with an extended caveat regarding the ethical dilemmas of this study. Finally, I offer a set of practical recommendations for different communities with which my research has been conversant, the fields of Rhetoric and Rhetoric of Health and Medicine, Disability Studies, and activism related to mental disabilities

Social Media Text Mining Framework for Drug Abuse: An Opioid Crisis Case Analysis

Social media is considered as a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes and behaviors, and medications. The daily use of social media provides new opportunities for analyzing several aspects of communication. Social media as a big data source can be used to recognize communication and behavioral themes of problematic use of prescription drugs. Mining and analyzing such media have challenges and limitations with respect to topic deduction and data quality. There is a need for a structured approach to efficiently and effectively analyze social media content related to drug abuse in a manner that can mitigate the challenges surrounding the use of this data source. Following a design science research methodology, the research aims at developing and evaluating a framework for mining and analyzing social media content related to drug abuse in a manner that will mitigate challenges and limitations related to topic deduction and data quality. The framework consists of four phases: Topic Discovery and Detection; Data Collection; Data Preparation and Quality; and Analysis and Results. The topic discovery and detection phase consists of a topic expansion stage for the drug abuse related topics that address the research domain and objectives. The topic expansion is based on different terms related to keywords, categories, and characteristics of the topic of interest and the objective of monitoring. To formalize the process and supporting artifacts, we create an ontology for drug abuse that captures the different categories that exist in the topic expansion and the literature. The data collection phase is characterized by the date range, social media platforms, search keywords, and a set of inclusion/exclusion criteria. The data preparation and quality phase is mainly concerned with obtaining high-quality data to mitigate problems with data veracity. In this phase, we pre-process the collected data then we evaluate the quality of the data, with respect to the terms and objectives of the research topic phase, using a data quality evaluation matrix. Finally, in the data analysis phase, the researcher can choose the suitable analysis approach. We used a combination of unsupervised and supervised machine learning approaches, including opinion and content analysis modeling. We demonstrate and evaluate the applicability of the proposed framework to identify common concerns toward opioid crisis from two perspectives; the addicted users’ perspective and the public’s (non-addicted users) perspective. In both cases, data is collected from twitter using Crimson Hexagon, a social media analytics tool for data collection and analysis. Natural language processing is used for data preparation and pre-processing. Different data visualization techniques such as, word clouds and clustering visualization, are used to form a deeper understanding of the relationships among the identified themes for the selected communities. The results help in understanding concerns of the public and opioid addicts towards the opioid crisis in the United States. Results of this study could help in understanding the problem aspects and provide key input when it comes to defining and implementing innovative solutions/strategies to face the opioid epidemic. From a theoretical perspective, this study highlights the importance of developing and adapting text mining techniques to social media for drug abuse. This study proposes a social media text mining framework for drug abuse research which lead to a good quality of datasets. Emphasis is placed on developing methods for improving the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and a lack of commonly available dictionary/language by the community such as in the opioid and drug abuse case. From a practical perspective, automatically analyzing social media users’ posts using machine learning tools can help in understanding the public themes and topics that exist in the recent discussions of online users of social media networks. This could help in developing proper mitigation strategies. Examples of such strategies can be gaining insights from the discussion topics to make the opioid media campaigns more effective in preventing opioid misuse. Finally, the study helps address some of the U.S. Department of Health and Human Services (HHS) five-point strategy by providing a systematic approach that could support conducting better research on addiction and drug abuse and strengthening public health data reporting and collection using social media data

Policy Development: Stress Management and Critical Incident Debriefing

Abstract Workplace stress and associated disorders (occupational burnout, compassion fatigue, secondary traumatic stress, critical incident stress, posttraumatic stress disorder, etc.) disproportionately affect healthcare workers, especially those working in critical care and emergency environments. The financial cost of stress related after-effects experienced by health care workers exceeds $191 billion each year and includes the cost of associated decreased quality of patient care, missed diagnoses, medical errors, and sentinel events leading to patient disablement or mortality. Mental health interventions such as stress management education and critical incident debriefings have been proven effective in reducing workplace stress and building personal resilience. A gap in practice was identified in the lack of a formal stress management education process in the participating facility. The purpose of this DNP project was to obtain consensus from a multidisciplinary panel of content experts to determine pertinent components for inclusion in a Stress Management and Critical Incident policy brief. The theoretical model guiding this project was the transactional model of stress and coping, which provides an interactive approach to developing coping skills and resiliency. This policy draft may be used to develop a formal program of stress management education for leadership and staff, critical incident debriefing, and institutional changes to promote a safe and effective work environment. Keywords: stress, critical incident, debriefing, occupational burnout, compassion fatigue, secondary traumatic stress, posttraumatic stress disorder, coping, resilienc