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. METHODS:This study examined variation in reports of engagement in cultural conversations during sessions (N=10,731) with 1,997 clients and 72 therapists from a university counseling center. Data were analyzed by using Bayesian multilevel models. RESULTS:Overall, clients reported having cultural conversations in 48.4% of sessions. Cultural conversations were much more likely to occur in sessions with BIPOC clients than with White clients: 66.2% of sessions with BIPOC clients involved conversations about cultural identities, compared with only 39.8% of sessions with White clients. Of note, the magnitude of this difference varied by therapist. CONCLUSION:Cultural conversations were more likely to occur in treatment with BIPOC clients than with White clients, and the presence of cultural conversations in treatment varied by therapist.

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. Utilizing 103,170 labeled talk turns from 188 psychotherapy sessions, we developed NLP models that recognized the discussion of cultural topics in psychotherapy (F - 1 = 70.0; Spearman's rho = 0.78, p < .001). We discuss implications for research and practice and applications for future NLP-based feedback tools.

The discussion of the influence of culture in psychotherapy is expanding to honor and incorporate the ways identities intersect within complex social systems. Some clients present for therapy with two or more identities that are in conflict, whereby the values or needs associated with different parts of the self are at odds. The resulting tension can be a significant driver of distress. This study sought to investigate therapist variability in facilitating change with clients depending on the interaction of their sexual orientation and the role of religion in their life (RR). We analyzed the depression scores of clients (n = 1,792) who received care at a university counseling center. After controlling for clients' pretherapy depression scores, the association between their sexual orientation and their posttherapy depression varied across therapists; however, the association between their RR and posttherapy depression did not. We also found that the association between the interaction of clients' sexual orientation and RR, and posttherapy depression varied across therapists. Therefore, some therapists had clients who experienced more or less change in their depression and that variability was predicted by the identity combinations clients endorsed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Background: The grief that accompanies witnessing the death of a child puts health care professionals at risk of secondary trauma, burnout, and turnover when left unaddressed. Objective: Support staff well-being and promote resiliency. Methods: Descriptive implementation of a structured, peer-to-peer bereavement support program for intensive care unit (ICU) staff at a tertiary children's hospital. Results: Thirty-five virtual sessions were held over the period of one year.Through these sessions, participants shared perspectives and normalized reactions, and explored potential coping strategies. Post-session feedback surveys demonstrated the negative impact of a death on the personal or work life of ICU staff. Additionally, nearly all reported some level of burnout. Conclusions: The sessions were feasible and positively impacted staff coping and well-being. Barriers and facilitators to session attendance, as well as suggestions for improvement, were also explored. Implications for practice and future research are discussed. No clinical trial registration is applicable.

Interactive software offers the potential to improve the quality of mental health care by promoting skill development, clinical insight, and shared reflection among mental health professionals. However, research on therapists' usage of interactive software has been primarily based in training and mock-clinical settings. In this paper, we present a qualitative study of clinicians' experience using CORE-MI, a collaborative recording and annotation software, in a real-world university counseling center. We describe how and why therapists, both trainees and licensed staff, used CORE-MI to support their practice and improve their skills. We also consider how usage varied with therapists' developmental level and how the software impacted relationships between supervisors and supervisees. Finally, we offer recommendations for future research and design of interactive systems in mental health settings.

With the growing prevalence of psychological interventions, it is vital to have measures which rate the e ff ectiveness of psychological care, in order 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 which leads to poor feasibility and limited use in real-world settings. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called Motivational Interviewing, our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist’s vs. client’s talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients’ perspective). We describe our platform and its performance, using a dataset of more than 5,000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. We are confident that a widespread use of automated psychotherapy rating tools in the near future will augment experts’ capabilities by providing an avenue for more e ff ective training and skill improvement and will eventually lead to more positive clinical outcomes.

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. In this study, we used a repeated measures design of 8,497 observations from 1,458 clients across 35 therapists to clarify the proportion of variability in MCC ratings attributed to the therapist versus the client and better understand the extent that an MCC measure detects therapist differences. Overall, we found that a small amount of variability in MCC ratings was attributed to the therapist (2%) and substantial amount attributed to the client (70%). These findings suggest that our measure of MCC primarily detected differences at the client level versus therapist level, indicating that therapist MCC scores were largely dependent on the client. Clinical implications and recommendations for future MCC research and measurement are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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. To facilitate this process, we have developed an automated competency rating tool able to process the raw recorded audio of a session, analyzing who spoke when, what they said, and how the health professional used language to provide therapy. Focusing on a use case of a specific type of psychotherapy called “motivational interviewing”, our system gives comprehensive feedback to the therapist, including information about the dynamics of the session (e.g., therapist’s vs. client’s talking time), low-level psychological language descriptors (e.g., type of questions asked), as well as other high-level behavioral constructs (e.g., the extent to which the therapist understands the clients’ perspective). We describe our platform and its performance using a dataset of more than 5000 recordings drawn from its deployment in a real-world clinical setting used to assist training of new therapists. Widespread use of automated psychotherapy rating tools may augment experts’ capabilities by providing an avenue for more effective training and skill improvement, eventually leading to more positive clinical outcomes.

A cultural opportunity is 1 of 3 pillars within multicultural orientation framework; it is defined as a moment in therapy when aspects of a client's background emerge, which can be deeply explored to better understand the salient aspects of a client's cultural identities. Research on cultural opportunities provides evidence that clients desire cultural conversations. However, no study to date has examined what cultural opportunities sound like in therapy and how therapists and clients utilize these opportunities. Accordingly, the purpose of this study was to examine the ways in which cultural conversations emerge during the first psychotherapy session and how clients and therapists engage in these cultural conversations. Psychotherapy sessions from diverse therapist-client pairings at a university counseling center (n = 22) were analyzed using (reflexive) thematic analysis. Qualitative findings revealed 4 themes around how cultural opportunities emerge (e.g., windowpane of feeling) and 3 themes in how they are responded to (e.g., look out the same window: using client's language to explore culture). Implications for therapist training and supervision are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

In a recent study involving routine outcome monitoring (ROM), Burlingame and colleagues (2018) found that the clients in group treatment yielded significantly more not-on-track (NOT) progress alerts during treatment compared to individual treatment. Additionally, nonequivalence was found in the timing of the first NOT alerts, with group treatment's first alerts occurring two sessions later than individual treatment. Because past research has generally demonstrated equivalence between the effects of individual and group therapy, the current study aims to determine whether these rate and timing differences are replicable. Without sufficient evidence that the NOT alerts' frequency and temporal patterns found previously are common across group therapies, we hypothesized that a new data source would show no difference between rate and timing of NOT alerts between group and individual therapy. Frequency and timing data of NOT alerts from archival Outcome Questionnaire administrations in a comparable counseling center (N = 5,639, M-age = 25.7, female = 58.4%. Caucasian = 78.4%) were analyzed and compared to Burlingame et al.'s (2018) results. The current study replicated the significant difference found in the rate of NOT alerts between treatment formats (p = .007). Additionally, the timing of NOT alerts created a more complex picture. Burlingame et al.'s (2018) results may be more common as preliminary results suggest that clients in group therapy are more likely to alert as NOT during the course of therapy when compared to clients in individual therapy. Implications of these findings are discussed.

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. Prediction of therapeutic alliance from session recordings is used as a case in point. Recordings from 1,235 sessions of 386 clients seen by 40 therapists at a university counseling center were processed using automatic speech recognition software. Machine learning algorithms learned associations between client ratings of therapeutic alliance exclusively from session linguistic content. Using a portion of the data to train the model, machine learning algorithms modestly predicted alliance ratings from session content in an independent test set (Spearman's ρ = .15, p < .001). These results highlight the potential to harness natural language processing and machine learning to predict a key psychotherapy process variable that is relatively distal from linguistic content. Six practical suggestions for conducting psychotherapy research using machine learning are presented along with several directions for future research. Questions of dissemination and implementation may be particularly important to explore as machine learning improves in its ability to automate assessment of psychotherapy process and outcome. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Child maltreatment (CM) lies on an extreme end of the continuum of parenting-at-risk, and while CM has been linked with a variety of behavioral indicators of dysregulation in children, less is known about how physiological markers of regulatory capacity contribute to this association. The present study examined patterns of mother and child physiological regulation and their relations with observed differences in parenting processes during a structured interaction. Abusing, neglecting, and non-CM mothers and their 3- to 5-year-old children completed a resting baseline and moderately challenging joint task. The structural analysis of social behavior was used to code mother-child interactions while simultaneous measures of respiratory sinus arrhythmia were obtained. Results indicated that physically abusive mothers were more likely to react to children's positive bids for autonomy with strict and hostile control, than either neglecting or non-CM mothers. CM exposure and quality of maternal responding to children's autonomous bids were uniquely associated with lower parasympathetic tone in children. Results provide evidence of neurodevelopmental associations between early CM exposure, the immediate interactive context of parenting, and children's autonomic physiology.