Mental health counseling is an enterprise with profound societ al importance where conversations play a primary role. In order to acquire the conversational skills needed to face a challenging range of situations, mental health counselors must rely on training and on continued experience with actual clients. However, in the absence of large scale longitudinal studies, the nature and significance of this developmental process remain unclear. For example, prior literature suggests that experience might not translate into consequential changes in counselor behavior. This has led some to even argue that counseling is a profession without expertise. In this work, we develop a computational framework to quantify the extent to which individuals change their linguistic behavior with experience and to study the nature of this evolution. We use our framework to conduct a large longitudinal study of mental health counseling conversations, tracking over 3,400 counselors across their tenure. We reveal that overall, counselors do indeed change their conversational behavior to become more diverse across interactions, developing an individual voice that distinguishes them from other counselors. Furthermore, a finer-grained investigation shows that the rate and nature of this diversification vary across functionally different conversational components.
Language in the mental health domain Prior literature has underlined the importance of linguistic choices and conversational behavior in mental health-related contexts. Qualitative @cite_4 @cite_41 @cite_26 @cite_16 and computational @cite_0 @cite_31 @cite_47 @cite_48 @cite_24 studies of dialogic interactions in counseling and psychotherapy have highlighted the potential benefits of conversational skills---such as a counselor's ability to reflect on a client's particular concerns. Other work has considered a broader range of settings such as online forums, exploring how psychological support is sought and provided @cite_40 @cite_42 @cite_1 @cite_49 @cite_21 @cite_36 . While these studies have focused on linguistic behaviors in the scope of single interactions, we seek to understand how these behaviors develop over time.
@cite_47 @cite_26 @cite_4 @cite_41 @cite_48 @cite_36 @cite_42 @cite_1 @cite_21 @cite_0 @cite_24 @cite_40 @cite_49 @cite_31 @cite_16
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Abstract Motivational interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors. Studies on MI fidelity and mechanisms of change typically use human raters to code therapy sessions, which requires considerable time, training, and financial costs. Natural language processing techniques have recently been utilized for coding MI sessions using machine learning techniques, rather than human coders, and preliminary results have suggested these methods hold promise. The current study extends this previous work by introducing two natural language processing models for automatically coding MI sessions via computer. The two models differ in the way they semantically represent session content, utilizing either 1) simple discrete sentence features (DSF model) and 2) more complex recursive neural networks (RNN model). Utterance- and session-level predictions from these models were compared to ratings provided by human coders using a large sample of MI sessions ( N =341 sessions; 78,977 clinician and client talk turns) from 6 MI studies. Results show that the DSF model generally had slightly better performance compared to the RNN model. The DSF model had "good" or higher utterance-level agreement with human coders (Cohen's kappa>0.60) for open and closed questions, affirm, giving information, and follow neutral (all therapist codes); considerably higher agreement was obtained for session-level indices, and many estimates were competitive with human-to-human agreement. However, there was poor agreement for client change talk, client sustain talk, and therapist MI-inconsistent behaviors. Natural language processing methods provide accurate representations of human derived behavioral codes and could offer substantial improvements to the efficiency and scale in which MI mechanisms of change research and fidelity monitoring are conducted. Professional training in motivational interviewing, as on many other topics, is often delivered via a one-time clinical workshop. To what extent do practitioners actually acquire skillfulness through such training? Twenty-two counselors participated in training, of whom 15 completed a study of changes in practice behavior up to 4 months after a motivational interviewing workshop. In addition to self-report questionnaires, they provided taped practice samples before and after training, which were coded for counselor and client behavior. On paper-and-pencil measures, participants reported large increases in motivational interviewing skills. Observational measures reflected more modest changes in practice behavior that were often retained 4 months after training. Clients, however, did not show the response changes that have been found to be predictive of better outcomes with motivational interviewing. While practice behavior changed to a statistically significant extent, the effect of training was apparently not large enough to make a difference in client response. Possible implications for training and quality control of psychotherapies are considered. Mental illness is one of the most pressing public health issues of our time. While counseling and psychotherapy can be effective treatments, our knowledge about how to conduct successful counseling conversations has been limited due to lack of large-scale data with labeled outcomes of the conversations. In this paper, we present a large-scale, quantitative study on the discourse of text-message-based counseling conversations. We develop a set of novel computational discourse analysis methods to measure how various linguistic aspects of conversations are correlated with conversation outcomes. Applying techniques such as sequence-based conversation models, language model comparisons, message clustering, and psycholinguistics-inspired word frequency analyses, we discover actionable conversation strategies that are associated with better conversation outcomes. Clinical psychology literature indicates that reframing ir- rational thoughts can help bring positive cognitive change to those suffering from mental distress. Through data from an online mental health forum, we study how these cognitive processes play out in peer-to-peer conversations. Acknowledging the complexity of measuring cognitive change, we first provide an operational definition of a "moment of change" based on sentiment change in online conversations. Using this definition, we propose a predictive model that can identify whether a conversation thread or a post is associated with a moment of cognitive change. Consistent with psychological literature, we find that markers of language associated with sentiment and and affect are the most predictive. Further, cultural differences play an important role: predictive models trained on one country generalize poorly to others. To understand how a moment of change happens, we build a model that explicitly tracks topic and associated sentiment in a forum thread. Self-disclosure of stigmatized conditions is known to yield therapeutic benefits. Social media sites are emerging as promising platforms enabling disclosure around a variety of stigmatized concerns, including mental illness. What kind of behavioral changes precede and follow such disclosures? Do the therapeutic benefits of "opening up" manifest in these changes? In this paper, we address these questions by focusing on disclosures of schizophrenia diagnoses made on Twitter. We adopt a clinically grounded quantitative approach to first identify temporal phases around disclosure during which symptoms of schizophrenia are likely to be significant. Then, to quantify behaviors before and after disclosures, we define linguistic measures drawing from literature on psycholinguistics and the socio-cognitive model of schizophrenia. Along with significant linguistic differences before and after disclosures, we find indications of therapeutic outcomes following disclosures, including improved readability and coherence in language, future orientation, lower self preoccupation, and reduced discussion of symptoms and stigma perceptions. We discuss the implications of social media as a new therapeutic tool in supporting disclosures of stigmatized conditions. Participants in online communities often enact different roles when participating in their communities. For example, some in cancer support communities specialize in providing disease-related information or socializing new members. This work clusters the behavioral patterns of users of a cancer support community into specific functional roles. Based on a series of quantitative and qualitative evaluations, this research identified eleven roles that members occupy, such as welcomer and story sharer. We investigated role dynamics, including how roles change over members' lifecycles, and how roles predict long-term participation in the community. We found that members frequently change roles over their history, from ones that seek resources to ones offering help, while the distribution of roles is stable over the community's history. Adopting certain roles early on predicts members' continued participation in the community. Our methodology will be useful for facilitating better use of members' skills and interests in support of community-building efforts. Background: Behavioral interventions such as psychotherapy are leading, evidence-based practices for a variety of problems (e.g., substance abuse), but the evaluation of provider fidelity to behavioral interventions is limited by the need for human judgment. The current study evaluated the accuracy of statistical text classification in replicating human-based judgments of provider fidelity in one specific psychotherapy—motivational interviewing (MI). Method: Participants (n = 148) came from five previously conducted randomized trials and were either primary care patients at a safety-net hospital or university students. To be eligible for the original studies, participants met criteria for either problematic drug or alcohol use. All participants received a type of brief motivational interview, an evidence-based intervention for alcohol and substance use disorders. The Motivational Interviewing Skills Code is a standard measure of MI provider fidelity based on human ratings that was used to evaluate all therapy sessions. A text classification approach called a labeled topic model was used to learn associations between human-based fidelity ratings and MI session transcripts. It was then used to generate codes for new sessions. The primary comparison was the accuracy of model-based codes with human-based codes. Results: Receiver operating characteristic (ROC) analyses of model-based codes showed reasonably strong sensitivity and specificity with those from human raters (range of area under ROC curve (AUC) scores: 0.62 – 0.81; average AUC: 0.72). Agreement with human raters was evaluated based on talk turns as well as code tallies for an entire session. Generated codes had higher reliability with human codes for session tallies and also varied strongly by individual code. Conclusion: To scale up the evaluation of behavioral interventions, technological solutions will be required. The current study demonstrated preliminary, encouraging findings regarding the utility of statistical text classification in bridging this methodological gap. Online social support is known to play a significant role in mental well-being. However, current research is limited in its ability to quantify this link. Challenges exist due to the paucity of longitudinal, pre- and post mental illness risk data, and reliable methods that can examine causality between past availability of support and future risk. In this paper, we propose a method to measure how the language of comments in Reddit mental health communities influences risk to suicidal ideation in the future. Incorporating human assessments in a stratified propensity score analysis based framework, we identify comparable subpopulations of individuals and measure the effect of online social support language. We interpret these linguistic cues with an established theoretical model of social support, and find that esteem and network support play a more prominent role in reducing forthcoming risk. We discuss the implications of our work for designing tools that can improve support provisions in online communities. For online communities to be successful, they must retain an adequate number of members who contribute to the community. The amount and type of communication members receive can play an important role in generating and sustaining members' commitment to it. However, the communication that members find valuable may change with their tenure in the community. This paper examines how the communication members receive in an health support community influences their commitment and how this influence changes with their tenure in the community. Commitment was operationalized with three measures: self-reported attachment, continued participation in the community, and responding to others. Results show that receiving communication was generally associated with increased commitment across the three measures, with its impact increasing with members' tenure. However, the average amount of informational and emotional support members received per message was associated with decreased commitment. Results have implications for interventions to encourage members' commitment to their communities throughout their history in the community. Motivational Interviewing (MI) is an efficacious treatment for substance use disorders and other problem behaviors (Lundahl and Burke, 2009). However, little is known about the specific mechanisms that drive therapeutic change. A growing body of research has focused on coding within-session language to better understand how therapist and patient language mutually influence each other and predict successful (or unsuccessful) treatment outcomes. These studies typically use human raters, requiring considerable financial, time, and training costs for conducting such research. This paper describes the development and testing of a recursive neural network (RNN) model for rating 78,977 therapist and patient talk turns across 356 MI sessions. We assessed the accuracy of RNNs in predicting human ratings for client speech and compared them to standard n-gram models. The RNN model showed improvement over ngram models for some codes, but overall, all of the models performed well below human reliability, demonstrating the difficulty of the task. The process of eliciting client language toward change (change talk [CT]) is implicated as a causal mechanism in motivational interviewing (MI) and brief motivational interventions (BMI). We investigated the articulation of counselor behaviors and CT during BMI with young men. We coded 149 sessions using the Motivational Interviewing Skill Code and summarized these codes into three counselor categories (MI-consistent [MICO], MI-inconsistent [MIIN], other) and three client categories (CT, counter CT [CCT], follow neutral [F N]). We then computed immediate transition frequencies and odds ratios using sequential analysis software. CT was significantly more likely following MICO behaviors, whereas MIIN behaviors only led to CCT and F N. This strongly supports the use of MI skills to elicit CT during BMI with young men, whose speech also predicted counselor behaviors (particularly CT to MICO and CCT to MIIN). Additional analyses showed that among MICO behaviors, reflective listening may be a particularly powerful technique to elicit CT.