You never know what you are going to get: Large-scale assessment of therapists’ supportive counseling skill use.

Zhang, Xinyao; Tanana, Michael; Weitzman, Lauren M.; Narayanan, Shrikanth; Atkins, David C.; Imel, Zac E.

doi:10.1037/pst0000460

Cited by 3 publications

(1 citation statement)

References 73 publications

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“…Further, by using extant audiovisual recordings, we had the opportunity to study involvement as it “interacts with the therapist techniques in an ongoing, cyclical, and facilitative process” (Morris et al, 2016, p. 75). With this approach, this study differs from most research on children’s involvement in child and family therapies and in allied fields as they have been based on retrospective or altered sources of practice (Toros, 2021; van Bijleveld et al, 2015; Zhang et al, 2023) and/or attended to the effects of involvement (e.g., Skauge et al, 2021) instead of the interactive processes in the practices themselves (cf. Midgley, 2004).…”

Section: Children’s In-session Involvement In Child and Family Therapiesmentioning

confidence: 99%

Facilitating children’s in-session involvement in child and family therapies: A dynamic framework of clinical practices.

Edman,

Gustafsson,

Cuadra

2024

Psychotherapy

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Children’s in-session involvement in child and family therapies correlates with both positive and negative treatment outcomes. Thus, it is important to gain a better understanding of the clinical practices that facilitate children’s involvement in therapy sessions so that practitioners can employ them with greater precision. To address this need, we conducted a study to answer the following question: What clinical practices facilitate children’s in-session involvement in child and family therapies? The data consisted of 16 extant audiovisual recordings of child and family therapy sessions and 24 stimulated-recall interviews with the participants in the recordings. Following constructivist grounded theory and incorporating storyline as an additional analytical technique, we have constructed a framework consisting of four involvement-enhancing practices: managing time, staying relevant, adjusting intensity, and facilitating inclusion. Furthermore, by detailing some of the complex processes that practitioners navigate when they facilitate children’s involvement, our study adds a multilayered and dynamic dimension to the list of already established involvement facilitators. It may be used to moderate an overstandardized work culture that continues to characterize services that address children’s needs. The results may be applied to other institutional encounters, providing resonance beyond the analyzed therapy sessions.

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Section: Children’s In-session Involvement In Child and Family Therapiesmentioning

confidence: 99%

Facilitating children’s in-session involvement in child and family therapies: A dynamic framework of clinical practices.

Edman,

Gustafsson,

Cuadra

2024

Psychotherapy

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Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation

Stade,

Stirman,

Ungar

et al. 2024

npj Mental Health Res

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Large language models (LLMs) such as Open AI’s GPT-4 (which power ChatGPT) and Google’s Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.

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Action recommendations review in community-based therapy and depression and anxiety outcomes: a machine learning approach

Spinrad,

Taylor,

Ruzek

et al. 2024

BMC Psychiatry

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Background While the positive impact of homework completion on symptom alleviation is well-established, the pivotal role of therapists in reviewing these assignments has been under-investigated. This study examined therapists' practice of assigning and reviewing action recommendations in therapy sessions, and how it correlates with patients’ depression and anxiety outcomes. Methods We analyzed 2,444 therapy sessions from community-based behavioral health programs. Machine learning models and natural language processing techniques were deployed to discern action recommendations and their subsequent reviews. The extent of the review was quantified by measuring the proportion of session dialogues reviewing action recommendations, a metric we refer to as “review percentage”. Using Generalized Estimating Equations modeling, we evaluated the correlation between this metric and changes in clients' depression and anxiety scores. Results Our models achieved 76% precision in capturing action recommendations and 71.1% in reviewing them. Using these models, we found that therapists typically provided clients with one to eight action recommendations per session to engage in outside therapy. However, only half of the sessions included a review of previously assigned action recommendations. We identified a significant interaction between the initial depression score and the review percentage (p = 0.045). When adjusting for this relationship, the review percentage was positively and significantly associated with a reduction in depression score (p = 0.032). This suggests that more frequent review of action recommendations in therapy relates to greater improvement in depression symptoms. Further analyses highlighted this association for mild depression (p = 0.024), but not for anxiety or moderate to severe depression. Conclusions An observed positive association exists between therapists’ review of previous sessions’ action recommendations and improved treatment outcomes among clients with mild depression, highlighting the possible advantages of consistently revisiting therapeutic homework in real-world therapy settings. Results underscore the importance of developing effective strategies to help therapists maintain continuity between therapy sessions, potentially enhancing the impact of therapy.

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You never know what you are going to get: Large-scale assessment of therapists’ supportive counseling skill use.

Cited by 3 publications

References 73 publications

Facilitating children’s in-session involvement in child and family therapies: A dynamic framework of clinical practices.

Facilitating children’s in-session involvement in child and family therapies: A dynamic framework of clinical practices.

Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation

Action recommendations review in community-based therapy and depression and anxiety outcomes: a machine learning approach

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