Real-world stress and control: integrating ambulatory physiological and ecological momentary assessment technologies to explain daily wellbeing.

The current study sought to advance our understanding of the connections between stress, perceived control, affect, and physiology in daily life. To achieve this goal, we integrated hourly ambulatory physiological and experiential data from young adult participants who experienced work or academic stressors over the course of a day. Participants wore a cardiovascular monitor that recorded heart rate data continuously for 8 h while hourly random Ecological Momentary Assessment (EMA) data were collected in personally relevant settings via mobile phones to learn about stress, perceived control, and affect.

Association of machine-learning-rated supportive counseling skills with psychotherapy outcome.

Objective: This study applied a machine-learning-based skill assessment system to investigate the association between supportive counseling skills (empathy, open questions, and reflections) and treatment outcomes. We hypothesized that higher empathy and higher use of open questions and reflections would be associated with greater symptom reduction.

Method: We used a data set with 2,974 sessions, 610 clients, and 48 therapists collected from a university counseling center, which included 845,953 rated therapist statements.

Cultural Conversations in Therapy: How Often Clients Talk About Their Identities.

Objective: The purpose of this study was to examine how often clients report discussing cultural identities during counseling sessions; the extent to which discussion of cultural identities during treatment varies across therapists; whether identifying as BIPOC (Black, Indigenous, and people of color) predicts clients' discussion of cultural identities in sessions; and whether differences in the frequency of cultural conversations (i.e., dialogue that focuses on client cultural identities) across client groups depend on the therapist.

Identification of cultural conversations in therapy using natural language processing models.

Researchers have historically focused on understanding therapist multicultural competency and orientation through client self-report measures and behavioral coding. While client perceptions of therapist cultural competency and multicultural orientation and behavioral coding are important, reliance on these methods limits therapists receiving systematic, scalable feedback on cultural opportunities within sessions. Prior research demonstrating the feasibility of automatically identifying topics of conversation in psychotherapy suggests that natural language processing (NLP) models could be trained to automatically identify when clients and therapists are talking about cultural concerns and could inform training and provision of rapid feedback to therapists.

Machine-Learning-Based Prediction of Client Distress From Session Recordings.

Natural language processing (NLP) is a subfield of machine learning that may facilitate the evaluation of therapist-client interactions and provide feedback to therapists on client outcomes on a large scale. However, there have been limited studies applying NLP models to client outcome prediction that have (a) used transcripts of therapist-client interactions as direct predictors of client symptom improvement, (b) accounted for contextual linguistic complexities, and (c) used best practices in classical training and test splits in model development. Using 2,630 session recordings from 795 clients and 56 therapists, we developed NLP models that directly predicted client symptoms of a given session based on session recordings of the previous session (Spearman's rho =0.

Impact of racial microaggressions in psychotherapy vignettes with african american clients: An experimental analogue design.

Mental health researchers have focused on promoting culturally sensitive clinical care (Herman et al., 2007; Whaley & Davis, 2007), emphasizing the need to understand how biases may impact client well-being. Clients report that their therapists commit racial microaggressions-subtle, sometimes unintentional, racial slights-during treatment (Owen et al.

Automating the assessment of multicultural orientation through machine learning and natural language processing.

Recent scholarship has highlighted the value of therapists adopting a multicultural orientation (MCO) within psychotherapy. A newly developed performance-based measure of MCO capacities exists (MCO-performance task [MCO-PT]) in which therapists respond to video-based vignettes of clients sharing culturally relevant information in therapy. The MCO-PT provides scores related to the three aspects of MCO: cultural humility (i.

Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.

Importance: Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale.

Objective: To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care.

Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?

Background: The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings.

The Silent Treatment?: Changes in patient emotional expression after silence.

Psychotherapy can be an emotionally laden conversation, where both verbal and non-verbal interventions may impact the therapeutic process. Prior research has postulated mixed results in how clients emotionally react following a silence after the therapist is finished talking, potentially due to studying a limited range of silences with primarily qualitative and self-report methodologies. A quantitative exploration may illuminate new findings.

Facilitating dyadic synchrony in psychotherapy sessions: Systematic review and meta-analysis.

This paper highlights the facilitation of dyadic synchrony as a core psychotherapist skill that occurs at the non-verbal level and underlies many other therapeutic methods. We define dyadic synchrony, differentiate it from similar constructs, and provide an excerpt illustrating dyadic synchrony in a psychotherapy session. We then present a systematic review of 17 studies that have examined the associations between dyadic synchrony and psychotherapy outcomes.

Threat alert: The effect of outliers on the alliance-outcome correlation.

Meta-analyses have established the alliance as the most robust predictor of outcome in psychotherapy. A growing number of studies have evaluated potential threats to the conclusion that alliance is a factor in psychotherapy. One potential threat that has not been systematically examined is the possibility that the alliance-outcome association is driven by low alliance outliers.

Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions.

Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting. The quality of a CBT session is typically assessed by trained human raters who manually assign pre-defined session-level behavioral codes. In this paper, we develop an end-to-end pipeline that converts speech audio to diarized and transcribed text and extracts linguistic features to code the CBT sessions automatically.

Knowledge and Attitudes Toward an Artificial Intelligence-Based Fidelity Measurement in Community Cognitive Behavioral Therapy Supervision.

To capitalize on investments in evidence-based practices, technology is needed to scale up fidelity assessment and supervision. Stakeholder feedback may facilitate adoption of such tools. This evaluation gathered stakeholder feedback and preferences to explore whether it would be fundamentally feasible or possible to implement an automated fidelity-scoring supervision tool in community mental health settings.

Automated evaluation of psychotherapy skills using speech and language technologies.

With the growing prevalence of psychological interventions, it is vital to have measures which rate the effectiveness of psychological care to assist in training, supervision, and quality assurance of services. Traditionally, quality assessment is addressed by human raters who evaluate recorded sessions along specific dimensions, often codified through constructs relevant to the approach and domain. This is, however, a cost-prohibitive and time-consuming method that leads to poor feasibility and limited use in real-world settings.

How do you feel? Using natural language processing to automatically rate emotion in psychotherapy.

Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account.

Automated rating of patient and physician emotion in primary care visits.

Objective: Train machine learning models that automatically predict emotional valence of patient and physician in primary care visits.

Methods: Using transcripts from 353 primary care office visits with 350 patients and 84 physicians (Cook, 2002 [1], Tai-Seale et al., 2015 [2]), we developed two machine learning models (a recurrent neural network with a hierarchical structure and a logistic regression classifier) to recognize the emotional valence (positive, negative, neutral) (Posner et al.

Determining the therapist's contribution in therapist multicultural competence process and outcome.

Efforts to help therapists improve their multicultural competence (MCC) rely on measures that can distinguish between different levels of competence. MCC is often assessed by asking clients to rate their experiences with their therapists. However, differences in client ratings of therapist MCC do not necessarily provide information about the relative performance of therapists and can be influenced by other factors including the client's own characteristics.

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine learning is dramatically changing scientific research and industry and may also hold promise for addressing limitations encountered in mental health care and psychotherapy. The current paper introduces machine learning and natural language processing as related methodologies that may prove valuable for automating the assessment of meaningful aspects of treatment.

Coregulation of therapist and client emotion during psychotherapy.

Objective: Close interpersonal relationships are fundamental to emotion regulation. Clinical theory suggests that one role of therapists in psychotherapy is to help clients regulate emotions, however, if and how clients and therapists serve to regulate each other's emotions has not been empirically tested. Emotion coregulation - the bidirectional emotional linkage of two people that promotes emotional stability - is a specific, temporal process that provides a framework for testing the way in which therapists' and clients' emotions may be related on a moment to moment basis in clinically relevant ways.

Provider Contributions to Disparities in Mental Health Care.

Objective: Disparities in diagnosis of mental health problems and in access to treatment among racial-ethnic groups are apparent across different behavioral conditions, particularly in the quality of treatment for depression. This study aimed to determine how much disparities differ across providers.

Methods: Bayesian mixed-effects models were used to estimate whether disparities in patient adherence to antidepressant medication (N=331,776) or psychotherapy (N=275,095) were associated with specific providers.

Can a computer detect interpersonal skills? Using machine learning to scale up the Facilitative Interpersonal Skills task.

Therapist interpersonal skills are foundational to psychotherapy. However, assessment is labor intensive and infrequent. This study evaluated if machine learning (ML) tools can automatically assess therapist interpersonal skills.

The Structure of Competence: Evaluating the Factor Structure of the Cognitive Therapy Rating Scale.

The Cognitive Therapy Rating Scale (CTRS) is an observer-rated measure of cognitive behavioral therapy (CBT) treatment fidelity. Although widely used, the factor structure and psychometric properties of the CTRS are not well established. Evaluating the factorial validity of the CTRS may increase its utility for training and fidelity monitoring in clinical practice and research.