Therapist Behavior During Exposure Tasks Predicts Habituation and Clinical Outcome in Three Randomized Controlled Trials for Pediatric OCD

Benito, Kristen; Machan, Jason T.; Freeman, Jennifer B.; Garcia, Abbe; Walther, Michael; Frank, Hannah E.; Wellen, Brianna; Stewart, Elyse; Edmunds, Julie; Sapyta, Jeffrey; Franklin, Martin E.

doi:10.1016/j.beth.2020.07.004

Cited by 15 publications

(12 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is concerning for two reasons. First, it can be clinically iatrogenic by reinforcing a patient's perception that they are unable to tolerate their distress or handle the given situation [i.e., reduces intervention efficacy; (70)]. Second, the clinician also does not get the opportunity to see the full exposure effect occur (i.e., the patient ultimately is able to tolerate the distress and experience long-term symptom relief); this perpetuates the cycle of clinician avoidance.…”

Section: Exposure Therapymentioning

confidence: 99%

Clinician Maladaptive Anxious Avoidance in the Context of Implementation of Evidence-Based Interventions: A Commentary

et al. 2022

View full text Add to dashboard Cite

This paper posits that a clinician's own anxious reaction to delivering specific evidence-based interventions (EBIs) should be better accounted for within implementation science frameworks. A key next step for implementation science is to delineate the causal processes most likely to influence successful implementation of evidence-based interventions (EBIs). This is critical for being able to develop tailored implementation strategies that specifically target mechanisms by which implementation succeeds or fails. First, we review the literature on specific EBIs that may act as negatively valenced stimuli for clinicians, leading to a process of clinician maladaptive anxious avoidance that can negatively impact EBI delivery. In the following sections, we argue that there are certain EBIs that can cause emotional distress or discomfort in a clinician, related to either: (1) a clinicians' fear of the real or predicted short-term distress the EBI can cause patients, or (2) fears that the clinician will inadvertently cause the patient harm and/or face liability. This distress experienced by the clinician can perpetuate a cycle of maladaptive anxious avoidance by the clinician, contributing to lack of or suboptimal EBI implementation. We illustrate how this cycle of maladaptive anxious avoidance can influence implementation by providing several examples from leading EBIs in the psychosocial literature. To conclude, we discuss how leveraging decades of treatment literature aimed at mitigating maladaptive anxious avoidance can inform the design of more tailored and effective implementation strategies for EBIs that are negatively valenced.

show abstract

Section: Exposure Therapymentioning

confidence: 99%

Clinician Maladaptive Anxious Avoidance in the Context of Implementation of Evidence-Based Interventions: A Commentary

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Exposure represents a unique challenge for supervision in community settings (Becker-Haimes et al, 2020), owing to its complexity (Garcia, 2017), its tendency to evoke strong emotional reactions among clinicians (Deacon et al, 2013), and the high skill-level needed for therapists to tailor exposures to a client's core fears and encourage approach behaviors necessary for successful exposures (Benito et al, 2021). Even when there is sufficient time in supervision to discuss cases, there may be limited opportunities to brainstorm tailored exposures in depth, as the bulk of time may be spent focusing on updates from the previous week, other case management concerns, and administrative tasks.…”

Section: Exposure-specific Supervision Barriersmentioning

confidence: 99%

A case example of community-based supervision to overcome barriers and support the implementation of exposure therapy.

Schriger¹,

Boroshok²,

Khan³

et al. 2023

Psychological Services

View full text Add to dashboard Cite

Supervision in community mental health (CMH) settings is critically important for supporting clinical competency and optimizing clinical outcomes. However, CMH supervision is hindered by numerous administrative and fiscal challenges, which makes aligning supervision practices with gold-standard recommendations notoriously difficult. This article describes a case example of one multicomponent supervision approach within a CMH clinic used to support the delivery of exposure therapy ("exposure") to youth with publicly funded insurance. We first review current recommendations for supervision and highlight the primary barriers to the use of these supervisory practices in CMH settings. We then present three low-burden supervisory approaches used to supplement traditional individual supervision. Each strategy presented aims to mitigate known barriers and maximize alignment with gold-standard supervision practices for exposure while being sustainable within a community setting. Finally, we review the strengths and limitations of each supervision strategy. The multicomponent strategies presented represent a creative approach to CMH supervision that maximizes the limited resources and infrastructure in CMH settings to support the delivery of gold-standard supervision. We conclude by offering insights into how these strategies could support supervision for a broader range of evidencebased practices (EBPs) and make recommendations for future research. Impact StatementProviding effective supervision in publicly funded settings is notoriously challenging. This article describes how three low-burden supervisory approaches are used to supplement traditional individual supervision to enhance alignment of supervisory practices with gold-standard recommendations. The strengths and limitations of each approach are reviewed and guidance for how these strategies can be implemented in other settings is proposed.

show abstract

“…For example, motor unrest and slowness are symptoms of psychiatric disorders [2] [13]. Facial expressions, eye contact, proximity, touch, and gaze provide information about mental states such as emotions, attention, and cognitive effort, as well as interpersonal processes such as alliance, affection, and reciprocity [4] [8][9] [12]. While behavioral coding bypasses the problem of self-report bias, disadvantages also exist.…”

Section: Introductionmentioning

confidence: 99%

Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan

Lønfeldt¹,

Frumosu²,

Mora-Jensen³

et al. 2022

Preprint

View full text Add to dashboard Cite

MotivationBehavioral observations are an important resource in the study and evaluation of psychological phenomena, but it is costly, time-consuming, and susceptible to bias. Thus, we aim to automate coding of human behavior for use in psychotherapy and research with the help of artificial intelligence (AI) tools. Here, we present an analysis plan. Methods Videos of a gold-standard semi-structured diagnostic interview of 25 youth with obsessive-compulsive disorder (OCD) and 12 youth without a psychiatric diagnosis (no-OCD) will be analyzed. Youth were between 8 and 17 years old. Features from the videos will be extracted and used to compute ratings of behavior, which will be compared to ratings of behavior produced by mental health professionals trained to use a specific behavioral coding manual. We will test the effect of OCD diagnosis on the computationally-derived behavior ratings using multivariate analysis of variance (MANOVA). Using the generated features, a binary classification model will be built and used to classify OCD/no-OCD classes. DiscussionHere, we present a pre-defined plan for how data will be pre-processed, analyzed and presented in the publication of results and their interpretation. A challenge for the proposed study is that the AI approach will attempt to derive behavioral ratings based solely on vision, whereas humans use visual, paralinguistic and linguistic cues to rate behavior. Another challenge will be using machine learning models for body and facial movement detection trained primarily on adults and not on children. If the AI tools show promising results, this pre-registered analysis plan may help reduce interpretation bias. Trial registration: ClinicalTrials.gov -H-18010607 Keywords machine learning • visual signals from video data • human behavioral coding • children and adolescents • obsessive-compulsive disorder

show abstract

Therapist Behavior During Exposure Tasks Predicts Habituation and Clinical Outcome in Three Randomized Controlled Trials for Pediatric OCD

Cited by 15 publications

References 37 publications

Clinician Maladaptive Anxious Avoidance in the Context of Implementation of Evidence-Based Interventions: A Commentary

Clinician Maladaptive Anxious Avoidance in the Context of Implementation of Evidence-Based Interventions: A Commentary

A case example of community-based supervision to overcome barriers and support the implementation of exposure therapy.

Computational behavior recognition in child and adolescent psychiatry: A statistical and machine learning analysis plan

Contact Info

Product

Resources

About