Interventions: Drug: Suvorexant; Drug: Placebo; Behavioral: Prolonged Exposure Therapy for Primary Care (PE-PC)
Sponsors: Northern California Institute of Research and Education; Lowcountry Center for Veterans Research
Not yet recruiting
Institutional Members are clinics and programs in the US and around the globe that offer residential and/or intensive treatment for OCD and related disorders, are specialty outpatient clinics with a large staff dedicated to treating OCD, or provide low-cost treatment options through research studies.
Below are quarterly updates from our Institutional Members organized alphabetically. Click the (+) to open each menu and read updates and find contact information for clinics near you:
Do you work at a residential program, intensive outpatient program (ITP), or specialty outpatient clinic and looking to advertise your services? Learn more about becoming an Institutional Member and having your program updates included below!
The post Institutional Member Updates: Summer 2026 appeared first on International OCD Foundation.
Background: Generative artificial intelligence (GenAI) chatbots have the potential to provide personalized mental health support to individuals at scale. Objective: This study evaluates the feasibility and usage patterns of the Safespace GenAI chatbot, an artificial intelligence (AI)–driven smartphone app that offers a large language model–powered interactive chatbot to support mental health. Methods: Using a mixed methods approach, we explored baseline attitudes toward GenAI chatbots and chatbot usage patterns, conducted a qualitative content analysis of participants’ experiences, and descriptively assessed patterns related to preintervention depressive symptoms. The study included an initial sample of 42 university students, 20 of whom actively used the chatbot over 2 to 4 weeks, generating 286 user-chatbot interactions. Results: Preintervention surveys indicated that the majority of participants anticipated that the chatbot would be helpful (27/42, 64%) and that they trusted its privacy safeguards (39/42, 93%). Usage patterns suggested that the highest levels of interaction occurred early in the morning and late at night, when peer and professional support may be inaccessible. The qualitative analysis indicated that participants appreciated using the chatbot for reflection as a blended-care tool between their counseling sessions, while also naming technical barriers and specific design needs required to sustain engagement. In addition, our exploratory analyses descriptively showed that participants with elevated depression scores engaged in emotional disclosure during 99% (38 sessions with 8 participants) of their sessions, compared to 84% (26 sessions of 12 participants) of those with low symptoms. Due to the small sample size, future adequately powered studies are needed to inferentially examine these observed patterns. Conclusions: These findings provide initial insights into the usage and engagement dynamics of the Safespace GenAI chatbot and highlight directions for future research to optimize GenAI-driven mental health interventions. Trial Registration: AEA Registry AEARCTR-0013291; https://doi.org/10.1257/rct.13291-1.0
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Background: Motivational interviewing (MI) is an effective counseling approach for promoting health behavior change, but its scalability is constrained by the need for highly trained human counselors. Large language models (LLMs) may provide a scalable way to support MI counseling, but evidence remains limited, especially for Chinese MI resources and evaluations based on standardized MI fidelity frameworks. Objective: This study aimed to develop Chinese large language models for motivational interviewing (MI-LLMs) and evaluate whether MI-focused fine-tuning could improve their ability to generate counseling responses consistent with MI principles. Methods: We first curated 5 publicly available Chinese psychological counseling datasets and assessed sampled conversations in terms of comprehensiveness, professionalism, authenticity, and safety. The 2 highest-scoring datasets, CPsyCounD and PsyDTCorpus, were selected for MI-style data construction. Using GPT-4 with a structured MI-informed prompt, we transformed 2040 multiturn counseling conversations into MI-style dialogs. Among these, 2000 dialogs were used for training and 40 for testing. Three Chinese-capable open-source LLMs (Baichuan2-7B-Chat, ChatGLM-4-9B-Chat, and Llama-3-8B-Chinese-Chat-v2) were fine-tuned with low-rank adaptation on the training dataset and were referred to as MI-LLMs. Automatic evaluation was conducted on the testing dataset using Bilingual Evaluation Understudy–4 (BLEU-4) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Manual evaluation was conducted using the Motivational Interviewing Treatment Integrity Coding Manual 4.2.1. Thirty simulated counseling dialogs generated by the MI-LLMs were compared with 30 real MI dialogs sampled from AnnoMI and translated into Chinese. Two trained graduate student raters coded global scores and behavior counts, from which summary scores were subsequently calculated. Results: In automatic evaluation, fine-tuning substantially improved BLEU-4 and ROUGE scores across all 3 models compared with the base models. In manual evaluation, the MI-LLMs achieved technical and relational global scores, as well as total MI-adherent ratios that approached those of real MI dialogs. The MI-LLM based on ChatGLM-4-9B-Chat showed the strongest overall global performance. However, MI-LLMs produced fewer complex reflections and had lower reflection-to-question ratios than real MI dialogs. Conclusions: This study provides preliminary evidence that MI focused fine-tuning can help Chinese LLMs acquire core counseling behaviors consistent with MI principles. It also offers a scalable approach for constructing MI style dialog resources in Chinese. Nevertheless, current MI-LLMs should be regarded as early-stage tools for supporting, rather than replacing human counselors. Future work should expand real MI training data and strengthen the complex reflective skills of MI-LLMs. Further studies are needed to evaluate their effectiveness, acceptability, and safety in health behavior change settings in the real world.
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