Search

Objective: Counselor assessment of suicide risk is one key component of crisis counseling, and standards require risk assessment in every crisis counseling conversation. Efforts to increase risk assessment frequency are limited by quality improvement tools that rely on human evaluation of conversations, which is labor intensive, slow, and impossible to scale. Advances in machine learning (ML) have made possible the development of tools that can automatically and immediately detect the presence of risk assessment in crisis counseling conversations. Methods: To train models, a coding team labeled every statement in 476 crisis counseling calls (193,257 statements) for a core element of risk assessment. The authors then fine-tuned a transformer-based ML model with the labeled data, utilizing separate training, validation, and test data sets. Results: Generally, the evaluated ML model was highly consistent with human raters. For detecting any risk assessment, ML model agreement with human ratings was 98% of human interrater agreement. Across specific labels, average F1 (the harmonic mean of precision and recall) was 0.86 at the call level and 0.66 at the statement level and often varied as a result of a low base rate for some risk labels. Conclusions: ML models can reliably detect the presence of suicide risk assessment in crisis counseling conversations, presenting an opportunity to scale quality improvement efforts. [ABSTRACT FROM AUTHOR]

Objective: 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. Method: We then present a systematic review of 17 studies that have examined the associations between dyadic synchrony and psychotherapy outcomes. We also conduct a meta-analysis of 8 studies that examined whether there is more synchrony between clients and therapists than would be expected by chance. Results: Weighted box score analysis revealed that the overall association of synchrony and proximal as well as distal outcomes was neutral to mildly positive. The results of the meta-analysis indicated that real client-therapist dyad pairs exhibited synchronized behavioral patterns to a much greater extent than a sample of randomly paired people who did not actually speak. Conclusion: Our discussion revolves around how synchrony can be facilitated in a beneficial way, as well as situations in which it may not be beneficial. We conclude with training implications and therapeutic practices. [ABSTRACT FROM AUTHOR]

Psychotherapy can be an emotionally laden conversation, where both verbal and nonverbal interventions may impact the therapeutic process. Prior research has postulated mixed results regarding 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. Utilising research and automatic data processing from the field of linguistics, we analysed the full range of silence lengths (0.2–24.01 s), and measures of emotional expression—vocally encoded arousal and emotional valence from the works spoken—of 84 audio recordings of Motivational Interviewing sessions. We hypothesised that both the level and the variance of client emotional expression would change as a function of silence length; however, due to the mixed results in the literature, the direction of emotional change was unclear. We conducted a multilevel linear regression to examine how the level of client emotional expression changed across silence length, and an ANOVA to examine the variability of client emotional expression across silence lengths. Results of both analyses indicated that as silence length increased, emotional expression largely remained the same. Broadly, we demonstrated a weak connection between silence length and emotional expression, indicating no persuasive evidence that silence leads to client emotional processing and expression. [ABSTRACT FROM AUTHOR]

Psychotherapy is a conversation, whereby, at its foundation, many interventions are derived from the therapist talking. Research suggests that the voice can convey a variety of emotional and social information, and individuals may change their voice based on the context and content of the conversation (e.g. talking to a baby or delivering difficult news to patients with cancer). As such, therapists may adjust aspects of their voice throughout a therapy session depending on if they are beginning a therapy session and checking in with a client, conducting more therapeutic 'work' or ending the session. In this study, we modelled three vocal features—pitch, energy and rate—with linear and quadratic multilevel models to understand how therapists' vocal features change throughout a therapy session. We hypothesised that all three vocal features would be best fit with a quadratic function—starting high and more congruent with a conversational voice, decreasing during the middle portions of therapy where more therapeutic interventions were being administered, and increasing again at the end of the session. Results indicated a quadratic model for all three vocal features was superior in fitting the data, as compared to a linear model, suggesting that therapists begin and end therapy using a different style of voice than in the middle of a session. [ABSTRACT FROM AUTHOR]

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. Method: Utilizing 227 audio recordings from a relationally oriented treatment (Motivational Interviewing), we estimated continuous values of vocally encoded emotional arousal via mean fundamental frequency. We used dynamic systems models to examine emotional coregulation, and tested the hypothesis that each individual's emotional arousal would be significantly associated with fluctuations in the other's emotional state over the course of a psychotherapy session. Results: Results indicated that when clients became more emotionally labile over the course of the session, therapists became less so. When changes in therapist arousal increased, the client's tendency to become more aroused during session slowed. Alternatively, when changes in client arousal increased, the therapist's tendency to become less aroused slowed. [ABSTRACT FROM AUTHOR]

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.e., adoption of a nonsuperior and other-oriented stance toward clients), cultural opportunities (i.e., seizing or making moments in session to ask about clients' cultural identities), and cultural comfort (i.e., therapists' comfort in cultural conversations). Although a promising measure, the MCO-PT relies on labor-intensive human coding. The present study evaluated the ability to automate the scoring of the MCO-PT transcripts using modern machine learning and natural language processing methods. We included a sample of 100 participants ( n = 613 MCO-PT responses). Results indicated that machine learning models were able to achieve near-human reliability on the average across all domains (Spearman's ρ = .75, p < .0001) and opportunity (ρ = .81, p < .0001). Performance was less robust for cultural humility (ρ = .46, p < .001) and was poorest for cultural comfort (ρ = .41, p < .001). This suggests that we may be on the cusp of being able to develop machine learning-based training paradigms that could allow therapists opportunities for feedback and deliberate practice of some key therapist behaviors, including aspects of MCO. (PsycInfo Database Record (c) 2024 APA, all rights reserved).