About

Jennifer Freeman is a Professor of Psychiatry and Human Behavior (Research) at the Alpert Medical School of Brown University. She serves as the Director of the Pediatric Anxiety Research Center (PARC) at Bradley Hospital, where she has spent over two decades developing research, training, and clinical programs focused on pediatric obsessive-compulsive disorder (OCD) and anxiety. Her work emphasizes innovative care models, community training, and the integration of research and clinical practice within hospital settings. Freeman's research has been extensively funded by the National Institute of Mental Health (NIMH) and the Patient-Centered Outcomes Research Institute (PCORI), supporting studies on cognitive-behavioral treatment (CBT) for pediatric anxiety and OCD, including development of novel treatments, dissemination of effective training for community therapists, and long-term outcomes of early interventions. She has contributed significantly to understanding the principles of exposure therapy, treatment fidelity, and barriers to accessing evidence-based care, with a focus on translating research into high-quality, accessible services outside traditional clinical settings. Freeman also holds leadership roles in various committees and collaborates nationally and internationally to advance research and clinical practice in child and adolescent mental health.

Research topics

• Medicine
• Clinical psychology
• Psychology
• Psychotherapist
• Internal medicine
• Physical therapy
• Psychiatry

Selected publications

• Harnessing Natural Language Processing for Automated Exposure Therapy Coding in Youth with OCD

  2025-07-01

  preprintOpen access

  Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System quality codes -- specifically exposure and encourage events -- during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques.Methods: The system was trained and tested on 360 manually labeled pediatric OCD therapy sessions from three clinical trials. Audio data were processed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Manual transcriptions of two-minute audio segments were compared against ASR-generated transcripts to assess transcription accuracy via word error rate (WER). The resulting text was analyzed with transformer-based models, including BERT, SBERT, and Meta Llama 3. Two classification settings were explored: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model.Results: Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs. 0.51). In the sequence classification setting, Llama 3 models achieved high performance with AUC scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events.Conclusion: Automated quality coding of in-person exposure therapy sessions is feasible using current ASR and transformer-based models. These findings suggest potential for real-time quality assessment in clinical practice and scalable research on effective therapy methods. Finally, future work is needed for optimization, including improvements in ASR accuracy, expanded training datasets, and multimodal data integration.

  PublisherOA PDFDOI

• Enhancing Exposure Therapy Training through Virtual Reality Simulation: A Randomized Pilot Trial (Preprint)

  2025-07-03

  preprint

  <sec> <title>BACKGROUND</title> Despite robust empirical support, exposure-based CBT remains one of the least utilized evidence-based practices (EBPs) for anxiety disorders in typical practice settings. Research suggests providers’ negative beliefs about the risk of negative events during exposure delivery are a major predictor of its underutilization. Studies have demonstrated that incorporating experiential learning such as role-playing into conventional didactic training can reduce therapists’ negative beliefs. However, these methods face limitations in terms of accessibility, standardization, and fidelity to real-life experiences. Emerging evidence suggests virtual reality (VR) simulations may be an effective and scalable alternative for improving skills and attitudes pertinent to mental health treatment. </sec> <sec> <title>OBJECTIVE</title> This study examines the initial efficacy of a novel VR simulation-based exposure training program (SET-VRTM) based on (1) perceptions of usability, and (2) degree of change in therapist learning targets (i.e. knowledge, self-efficacy, attitudes). Clinician participants were randomly assigned to a low-immersion desktop version or a high-immersion head-mounted display (HMD) version of the SET-VRTM program to explore the influence of immersion on key outcomes. </sec> <sec> <title>METHODS</title> Clinician participants (N=41) were recruited from a variety of practice settings. Before randomization, both groups received conventional (4-hour) didactic training for exposure therapy. Next, groups were assigned to immersion modality (desktop or HMD) and began delivering exposures to a virtually simulated patient. Participants practiced titrating exposure intensity (increase, decrease, continue) based on real-time visual and auditory cues from the virtual patient. Participants completed three rounds of exposure delivery to a simulated patient and reviewed their decisions with feedback at the end of each round. Exposure knowledge, exposure self-efficacy, and beliefs about exposures were measured at baseline, post-didactic, and post-VR. Participants also rated the acceptability, usability, and real-world authenticity of VR exposure training. </sec> <sec> <title>RESULTS</title> Both groups (desktop, HMD) showed significant improvement in exposure knowledge (p&lt;.01; p&lt;.01), self-efficacy (p&lt;.01; p&lt;.01), and beliefs about exposure (p&lt;.01; p&lt;.01) between baseline and didactic training. There were no significant differences between the low and high immersion groups on any measure at baseline or after didactics. Both groups demonstrated significant improvement in exposure self-efficacy (p&lt;.01; p&lt;.01) and beliefs (P&lt;.001; p=.012) from post-didactic to post-exposure delivery. Neither showed improved knowledge from post-didactic to post-exposure delivery (p&gt;.05; p&gt;.05). Both groups gave highly positive ratings for the acceptability, usability, and authenticity of the simulated training experience. Taken together, results indicate that VR training significantly improved therapists’ self-efficacy and beliefs about exposures beyond gains from didactic training alone. </sec> <sec> <title>CONCLUSIONS</title> VR exposure therapy training is both well-received and effective in addressing clinician-level barriers to optimal exposure delivery. Supplementing conventional didactic training with experiential learning via VR sessions may be a promising next step in optimizing the standardization, scalability, and effectiveness of exposure training. </sec> <sec> <title>CLINICALTRIAL</title> Clinicaltrials.gov Identifier: NCT06706245 </sec>

  PublisherDOI

• Harnessing Natural Language Processing for Automated Exposure Therapy Coding in Youth with OCD

  2025-04-24

  preprintOpen access

  Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System quality codes -- specifically exposure and encourage events -- during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques.Methods: The system was trained and tested on 360 manually labeled pediatric OCD therapy sessions from three clinical trials. Audio data were processed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Manual transcriptions of two-minute audio segments were compared against ASR-generated transcripts to assess transcription accuracy via word error rate (WER). The resulting text was analyzed with transformer-based models, including BERT, SBERT, and Meta Llama 3. Two classification settings were explored: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model.Results: Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs. 0.51). In the sequence classification setting, Llama 3 models achieved high performance with AUC scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events.Conclusion: Automated quality coding of in-person exposure therapy sessions is feasible using current ASR and transformer-based models. These findings suggest potential for real-time quality assessment in clinical practice and scalable research on effective therapy methods. Finally, future work is needed for optimization, including improvements in ASR accuracy, expanded training datasets, and multimodal data integration.

  PublisherOA PDFDOI

• Automated classification of exposure and encourage events in speech data from pediatric OCD treatment

  JAMIA Open · 2025-10-06 · 1 citations

  articleOpen access

  Objective: To develop and evaluate an automated classification system for labeling Exposure Process Coding System (EPCS) quality codes-specifically exposure and encourage events-during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques. Materials and Methods: The system was trained and tested on 360 manually labeled pediatric Obsessive-Compulsive Disorder (OCD) therapy sessions from 3 clinical trials. Audio recordings were transcribed using ASR tools (OpenAI's Whisper and Google Speech-to-Text). Transcription accuracy was evaluated via word error rate (WER) on manual transcriptions of 2-minute audio segments compared against ASR-generated transcripts. The resulting text was analyzed with transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT), Sentence-BERT, and Meta Llama 3. Models were trained to predict EPCS codes in 2 classification settings: sequence-level classification, where events are labeled in delimited text chunks, and token-level classification, where event boundaries are unknown. Classification was performed either with fine-tuned transformer-based models, or with logistic regression on embeddings produced by each model. Results: With respect to transcription accuracy, Whisper outperformed Google Speech-to-Text with a lower WER (0.31 vs 0.51). For sequence classification setting, Llama 3 models achieved high performance with area under the ROC curve (AUC) scores of 0.95 for exposures and 0.75 for encourage events, outperforming traditional methods and standard BERT models. In the token-level setting, fine-tuned BERT models performed best, achieving AUC scores of 0.85 for exposures and 0.75 for encourage events. Discussion and Conclusion: Current ASR and transformer-based models enable automated quality coding of in-person exposure therapy sessions. These findings demonstrate potential for real-time assessment in clinical practice and scalable research on effective therapy methods. Future work should focus on optimization, including improvements in ASR accuracy, expanding training datasets, and multimodal data integration.

  PublisherOA PDFDOI

• Who calls, who engages? Families seeking treatment for anxiety and OCD

  The Brown University Child and Adolescent Behavior Letter · 2025-02-04

  articleOpen accessSenior author

  Pediatric anxiety is among the most common mental health diagnoses for American youth, yet few youths diagnosed with anxiety/obsessive‐compulsive disorder (OCD) receive treatment. The majority of parents nationwide report at least some difficulty accessing mental health care for their child. Within the state of Rhode Island, where 12.7% of youth experienced anxiety concerns during 2021, 59% of caregivers reported difficulty accessing mental health care of any kind (Child and Adolescent Health Measurement Initiative, 2021‐2022). Access to exposure‐based CBT (exposure therapy), despite strong evidence as a frontline treatment for anxiety/OCD, is especially limited.

  PublisherOA PDFDOI

• 2.37 Virtual Practice Makes Perfect: A Novel Virtual Reality Training Platform for Exposure Therapy

  Journal of the American Academy of Child & Adolescent Psychiatry · 2025-10-01

  article
  PublisherDOI

• What works for whom in pediatric OCD: description of causally interpretable meta-analysis methods and report on trial data harmonization

  Psychological Medicine · 2025-01-01

  reviewOpen access

  BACKGROUND: Improving patient outcomes will be enhanced by understanding "what works, for whom?" enabling better matching of patients to available treatments. However, answering this "what works, for whom?" question requires sample sizes that exceed those of most individual trials. Conventional methods for combining data across trials, including aggregate-data meta-analysis, suffer from key limitations including difficulty accounting for differences across trials (e.g., comparing "apples to oranges"). Causally interpretable meta-analysis (CI-MA) addresses these limitations by pairing individual-participant-data (IPD) across trials using advancements in transportability methods to extend causal inferences to clinical "target" populations of interest. Combining IPD across trials also requires careful acquisition and harmonization of data, a challenging process for which practical guidance is not well-described in the literature. METHODS: We describe methods and work to date for a large harmonization project in pediatric obsessive-compulsive disorder (OCD) that employs CI-MA. RESULTS: We review the data acquisition, harmonization, meta-data coding, and IPD analysis processes for Project Harmony, a study that (1) harmonizes 28 randomized controlled trials, along with target data from a clinical sample of treatment-seeking youth ages 4-20 with OCD, and (2) applies CI-MA to examine "what works, for whom?" We also detail dissemination strategies and partner involvement planned throughout the project to enhance the future clinical utility of CI-MA findings. Data harmonization took approximately 125 hours per trial (3,000 hours total), which was considerably higher than preliminary projections. CONCLUSIONS: Applying CI-MA to harmonize data has the potential to answer "what works for whom?" in pediatric OCD.

  PublisherOA PDFDOI

• A Team-Based Partner-Driven Model for Delivering Outpatient Exposure Treatment for Pediatric Anxiety and Obsessive-Compulsive Disorder

  JAACAP Open · 2024-08-23 · 1 citations

  articleOpen access1st authorCorresponding

  Objective: Anxiety disorders are among the most common and earliest forms of psychopathology, yet few providers in community practice settings use or are trained in evidence-based treatments (EBTs) for pediatric anxiety. Delivery of EBTs is further limited by the "provider-centered" manner in which they are often administered (ie, office-based). This paper presents the rationale, design, and methods for a team-based approach to the treatment of pediatric anxiety that was developed with substantial patient, caregiver, and community partner involvement, and that addresses quality and workforce issues inherent in the current child mental health crisis. Method: This study aims to compare team-based community delivered cognitive behavioral treatment (CBT) and office-based CBT for pediatric anxiety and obsessive-compulsive disorder (OCD) in a sample of 333 children and adolescents 5 to 18 years of age. Rather than reporting outcomes, the purpose of this paper is to spotlight study design, methods, and procedures, including processes for fostering and maintaining strong partner engagement, training strategies, supervision structures, and implementation of quality and fidelity monitoring tools. Discussion: Treatment delivered outside of a traditional office setting using a team-based approach has the potential to increase patient access to care. In addition to describing specific design considerations, we provide a roadmap for the integration of community-based partners and for rigorous supervision and quality monitoring. Future directions are discussed, particularly in the context of lack of access to care that has a longstanding disproportionate impact on youth of color and youth from low-income communities. Clinical trial registration information: Improving Access to Child Anxiety Treatment (IMPACT); https://clinicaltrials.gov/study/. Diversity & Inclusion Statement: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group.

  PublisherOA PDFDOI

• The Exposure Guide: A Practical Measure of Exposure Quality

  Behavior Therapy · 2024-11-27

  articleSenior author
  PublisherDOI

• Executive Functioning, Familial Accommodation, and Treatment Response in Youth with OCD and Comorbid ADHD

  Research Square · 2024-08-27

  preprintOpen access
  PublisherOA PDFDOI

Recent grants

• NIH Grant R01MH064188

  NIH · $1.6M · 2009

• Family Based Treatment of Early Childhood OCD

  NIH · $954k · 2007–2012

• CBT for Pediatric OCD: Effective Therapist Behaviors and Community Training Pilot

  NIH · $937k · 2012–2018

• Family Based Treatment of Early Childhood OCD

  NIH · $222k · 2007–2012

• NIH Grant R21MH096828

  NIH · $437k · 2014

Frequent coauthors

• Abbe Garcia

  Bradley Hospital

  243 shared

• Kristen Benito

  132 shared

• Martin E. Franklin

  72 shared

• Christine A. Conelea

  University of Minnesota System

  69 shared

• Hannah E. Frank

  Monash University

  53 shared

• Jeffrey Sapyta

  Duke University

  50 shared

• Christopher A. Flessner

  Kent State University

  46 shared

• Erin O’Connor

  John Brown University

  45 shared

Labs

• Pediatric Anxiety Research Center (PARC)PI