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., 2014). Yet, existing studies often rely on retrospective evaluations of clients and cannot establish the causal impact of varying ambiguity of microaggressions on clients. This study uses an experimental analogue design to examine offensiveness, emotional reactions, and evaluations of the interaction across three distinct levels of microaggression statements: subtle, moderate, and overt. We recruited 158 adult African American participants and randomly assigned them to watch a brief counseling vignette. We found significant differences between the control and three microaggression statements on all outcome variables. We did not find significant differences between the microaggression conditions. This study, in conjunction with previous correlational research, highlights the detrimental impact of microaggressions within psychotherapy, regardless of racially explicit content. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Gender differences in prosthesis-related outcomes among veterans: Results of a national survey of U.S. veterans.

BACKGROUND: Women with lower extremity amputations (LEAs) tend to have poorer prosthesis-related outcomes than men, although the literature is sparse. To our knowledge, there are no prior studies examining prosthesis-related outcomes of women veterans with LEAs. OBJECTIVE: To examine gender differences (overall and by type of amputation) among veterans who underwent LEAs between 2005 and 2018, received care at the Veterans Health Administration (VHA) prior to undergoing amputation, and were prescribed a prosthesis. It was hypothesized that compared to men, women would report lower satisfaction with prosthetic services, poorer prosthesis fit, lower prosthesis satisfaction, less prosthesis use, and worse self-reported mobility. Furthermore, it was hypothesized that gender differences in outcomes would be more pronounced among individuals with transfemoral than among those with transtibial amputations. DESIGN: Cross-sectional survey. Linear regressions were used to assess overall gender differences in outcomes and gender differences based on type of amputation in a national sample of veterans. SETTING: VHA medical centers. PARTICIPANTS: The sample consisted of 449 veterans who self-identified their gender (women = 165, men = 284) with transtibial (n = 236), transfemoral (n = 135), and bilateral LEAs (n = 68) including all amputation etiologies. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The Orthotics and Prosthetics User's Survey, Trinity Amputation and Prosthesis Experiences Scale, and Prosthetic Limb Users Survey of Mobility-Short Form were used to assess satisfaction with prosthetic services, prosthesis fit, prosthesis satisfaction, prosthesis use, and self-reported mobility. RESULTS: Women had poorer self-reported mobility than men (d = -0.26, 95% confidence interval -0.49 to -0.02, p < .05); this difference was small. There were no statistically significant gender differences in satisfaction with prosthetic services, prosthesis fit, prosthesis satisfaction, daily hours of prosthesis use, or by amputation type. CONCLUSIONS: Contrary to the hypothesis, prosthesis-related outcomes were similar between men and women with LEAs. Minimal differences may in part be due to receiving care from the VHA's integrated Amputation System of Care.

Centering and decentering client disclosures: A qualitative study of therapists' responses to clients' experiences of discrimination.

Clients who are societally marginalized because of their identities may seek support from therapists to process experiences of discrimination and oppression. Therapist navigation of these cultural conversations is a crucial skill. However, there is limited research examining how therapists immediately respond to clients when they disclose experiences of discrimination. In this study, 66 participants watched two brief videos of fictitious clients disclosing experiences of covert and overt discrimination. Participants watched one of two video vignettes, with one focusing on a White lesbian woman client and another focusing on a Black woman client. After watching each video, participants were asked to respond to clients as if they were the clients' therapist. We used reflexive thematic analysis to code participant responses, which spanned three themes: (a) invalidation and decentering of client's emotions, appraisals, and experiences; (b) incongruence and avoidance of client's language around identities; and (c) empathic attunement and centering of client emotions, appraisals, and experiences. While some participant responses were interpreted as emotionally aligning with the client, other participant responses were coded as minimizing or questioning client experiences of discrimination or using language incongruent with how the client self-identified. We discuss implications for research, training, and practice in navigating conversations about discrimination with psychotherapy clients. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

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. A partially mixed, sequential research method design was used including focus group discussions with community mental health therapists (n = 18) and clinical leadership (n = 12) to explore typical supervision practices, followed by discussion of an automated fidelity feedback tool embedded in a cloud-based supervision platform. Interpretation of qualitative findings was enhanced through quantitative measures of participants' use of technology and perceptions of acceptability, appropriateness, and feasibility of the tool. Initial perceptions of acceptability, appropriateness, and feasibility of automated fidelity tools were positive and increased after introduction of an automated tool. Standard supervision was described as collaboratively guided and focused on clinical content, self-care, and documentation. Participants highlighted the tool's utility for supervision, training, and professional growth, but questioned its ability to evaluate rapport, cultural responsiveness, and non-verbal communication. Concerns were raised about privacy and the impact of low scores on therapist confidence. Desired features included intervention labeling and transparency about how scores related to session content. Opportunities for asynchronous, remote, and targeted supervision were particularly valued. Stakeholder feedback suggests that automated fidelity measurement could augment supervision practices. Future research should examine the relations among use of such supervision tools, clinician skill, and client outcomes.

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. However, recent advances in technology in the area of machine learning algorithms, in particular natural language processing, have made it possible for mental health researchers to identify sentiment, or emotion, in therapist-client interactions on a large scale that would be unattainable with more traditional methods. As an attempt to extend prior findings from Tanana et al. (2016), we compared their previous sentiment model with a common dictionary-based psychotherapy model, LIWC, and a new NLP model, BERT. We used the human ratings from a database of 97,497 utterances from psychotherapy to train the BERT model. Our findings revealed that the unigram sentiment model (kappa = 0.31) outperformed LIWC (kappa = 0.25), and ultimately BERT outperformed both models (kappa = 0.48).

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. 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).

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. 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).