Mental Health Counseling From Conversational Content With Transformer-Based Machine Learning.

Importance: Use of asynchronous text-based counseling is rapidly growing as an easy-to-access approach to behavioral health care. Similar to in-person treatment, it is challenging to reliably assess as measures of process and content do not scale. Objective: To use machine learning to evaluate clinical content and client-reported outcomes in a large sample of text-based counseling episodes of care. Design, Setting, and Participants: In this quality improvement study, participants received text-based counseling between 2014 and 2019; data analysis was conducted from September 22, 2022, to November 28, 2023. The deidentified content of messages was retained as a part of ongoing quality assurance. Treatment was asynchronous text-based counseling via an online and mobile therapy app (Talkspace). Therapists were licensed to provide mental health treatment and were either independent contractors or employees of the product company. Participants were self-referred via online sign-up and received services via their insurance or self-pay and were assigned a diagnosis from their health care professional. Exposure: All clients received counseling services from a licensed mental health clinician. Main Outcomes and Measures: The primary outcomes were client engagement in counseling (number of weeks), treatment satisfaction, and changes in client symptoms, measured via the 8-item version of Patient Health Questionnaire (PHQ-8). A previously trained, transformer-based, deep learning model automatically categorized messages into types of therapist interventions and summaries of clinical content. Results: The total sample included 166 644 clients treated by 4973 therapists (20 600 274 messages). Participating clients were predominantly female (75.23%), aged 26 to 35 years (55.4%), single (37.88%), earned a bachelor's degree (59.13%), and were White (61.8%). There was substantial variability in intervention use and treatment content across therapists. A series of mixed-effects regressions indicated that collectively, interventions and clinical content were associated with key outcomes: engagement (multiple R = 0.43), satisfaction (multiple R = 0.46), and change in PHQ-8 score (multiple R = 0.13). Conclusions and Relevance: This quality improvement study found associations between therapist interventions, clinical content, and client-reported outcomes. Consistent with traditional forms of counseling, higher amounts of supportive counseling were associated with improved outcomes. These findings suggest that machine learning-based evaluations of content may increase the scale and specificity of psychotherapy research.

Implementation Science and Practice-Oriented Research: Convergence and Complementarity.

Implementation science is the scientific study of methods to promote the uptake of research findings and other evidence-based practices in routine care, with the goal of improving the quality and effectiveness of health services (Bauer et al., 2015). In addition to this common goal, practice-oriented psychotherapy research (and researchers) and implementation science (and scientists) share a common focus on the people and the places where treatment happens. Thus, there exists strong potential for combining these two approaches. In this article, we provide a primer on implementation science for psychotherapy researchers and highlight important areas and examples of convergence and complementarity between implementation science and practice-oriented psychotherapy research. Specifically, we (a) define and describe the core features of implementation science; (b) discuss similarities and areas of complementarity between implementation science and practice-oriented psychotherapy research; (c) discuss a case example that exemplifies the integration of implementation science and practice-oriented research; and (d) propose directions for future research and collaborations that leverage both implementation science and practice-oriented research.

Combining attachment-based family therapy and cognitive behavioral therapy to improve outcomes for adolescents with anxiety.

Increases in adolescent anxiety over the past several years suggest a need for trauma-informed, culturally responsive interventions that help teens cope with environmental stressors like those associated with the COVID-19 pandemic. Although abundant evidence supports the efficacy of cognitive behavioral therapy (CBT) in treating adolescent anxiety, not all teens respond positively to CBT. CBT does not typically include strategies that address important family factors that may be impacting the teen's functioning, such as the attachment relationship. Attachment-based family therapy (ABFT) addresses the attachment relationship and other factors that contribute to the adolescent's anxiety and related distress. By enhancing positive parenting behaviors, such as acceptance and validation of the adolescent's distress and promotion of their autonomy, ABFT sessions may repair the attachment relationship and increase the family's ability and willingness to engage in CBT tasks aimed at reducing anxiety. This theoretical paper describes the ABFT model and proposes that implementing ABFT sessions prior to CBT could result in better clinical outcomes for adolescents with anxiety disorders by improving the context within which the anxiety symptoms and treatment are experienced. Given that ABFT is sensitive and responsive to family and other contextual factors, adolescents from marginalized communities and those from less individualistic cultures may find the model to be more acceptable and appropriate for addressing factors related to their anxiety. Thus, a combined ABFT+CBT model might result in better outcomes for adolescents who have not historically responded well to CBT alone.

Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.

BACKGROUND: Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. ("Lyssn") a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform. METHODS: Phase I will work from an existing software prototype to develop a LyssnCBT user interface geared to the needs of community mental health (CMH) agencies. Core activities include a user-centered design focus group and interviews with community mental health therapists, supervisors, and administrators to inform the design and development of LyssnCBT. LyssnCBT will be evaluated for usability and implementation readiness in a final stage of Phase I. Phase II will conduct a stepped-wedge, hybrid implementation-effectiveness randomized trial (N = 1,875 clients) to evaluate the effectiveness of LyssnCBT to improve therapist CBT skills and client outcomes and reduce client drop-out. Analyses will also examine the hypothesized mechanism of action underlying LyssnCBT. DISCUSSION: Successful execution will provide automated, scalable CBT fidelity feedback for the first time ever, supporting high-quality training, supervision, and quality assurance, and providing a core technology foundation that could support the quality delivery of a range of EBPs in the future. TRIAL REGISTRATION: ClinicalTrials.gov; NCT05340738 ; approved 4/21/2022.

An Automated Quality Evaluation Framework of Psychotherapy Conversations with Local Quality Estimates.

Text-based computational approaches for assessing the quality of psychotherapy are being developed to support quality assurance and clinical training. However, due to the long durations of typical conversation based therapy sessions, and due to limited annotated modeling resources, computational methods largely rely on frequency-based lexical features or dialogue acts to assess the overall session level characteristics. In this work, we propose a hierarchical framework to automatically evaluate the quality of transcribed Cognitive Behavioral Therapy (CBT) interactions. Given the richly dynamic nature of the spoken dialog within a talk therapy session, to evaluate the overall session level quality, we propose to consider modeling it as a function of local variations across the interaction. To implement that empirically, we divide each psychotherapy session into conversation segments and initialize the segment-level qualities with the session-level scores. First, we produce segment embeddings by fine-tuning a BERT-based model, and predict segment-level (local) quality scores. These embeddings are used as the lower-level input to a Bidirectional LSTM-based neural network to predict the session-level (global) quality estimates. In particular, we model the global quality as a linear function of the local quality scores, which allows us to update the segment-level quality estimates based on the session-level quality prediction. These newly estimated segment-level scores benefit the BERT fine-tuning process, which in turn results in better segment embeddings. We evaluate the proposed framework on automatically derived transcriptions from real-world CBT clinical recordings to predict session-level behavior codes. The results indicate that our approach leads to improved evaluation accuracy for most codes when used for both regression and classification tasks.

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.

Assessing Causal Pathways and Targets of Implementation Variability for EBP use (Project ACTIVE): a study protocol.

BACKGROUND: Advancing causal implementation theory is critical for designing tailored implementation strategies that target specific mechanisms associated with evidence-based practice (EBP) use. This study will test the generalizability of a conceptual model that integrates organizational constructs and behavioral theory to predict clinician use of cognitive-behavioral therapy (CBT) techniques in community mental health centers. CBT is a leading psychosocial EBP for psychiatric disorders that remains underused despite substantial efforts to increase its implementation. METHODS: We will leverage ongoing CBT implementation efforts in two large public health systems (Philadelphia and Texas) to recruit 300 mental health clinicians and 600 of their clients across 40 organizations. Our primary implementation outcomes of interest are clinician intentions to use CBT and direct observation of clinician use of CBT. As CBT comprises discrete components that vary in complexity and acceptability, we will measure clinician use of six discrete components of CBT. After finishing their CBT training, participating clinicians will complete measures of organizational and behavior change constructs delineated in the model. Clinicians also will be observed twice via audio recording delivering CBT with a client. Within 48 h of each observation, theorized moderators of the intention-behavior gap will be collected via survey. A subset of clinicians who report high intentions to use CBT but demonstrate low use will be purposively recruited to complete semi-structured interviews assessing reasons for the intention-behavior gap. Multilevel path analysis will test the extent to which intentions and determinants of intention predict the use of each discrete CBT component. We also will test the extent to which theorized determinants of intention that include psychological, organizational, and contextual factors explain variation in intention and moderate the association between intentions and CBT use. DISCUSSION: Project ACTIVE will advance implementation theory, currently in its infancy, by testing the generalizability of a promising causal model of implementation. These results will inform the development of implementation strategies targeting modifiable factors that explain substantial variance in intention and implementation that can be applied broadly across EBPs.

Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions.

Cognitive Behavioral Therapy (CBT) is a goal-oriented psychotherapy for mental health concerns implemented in a conversational setting. The quality of a CBT session is typically assessed by trained human raters who manually assign pre-defined session-level behavioral codes. In this paper, we develop an end-to-end pipeline that converts speech audio to diarized and transcribed text and extracts linguistic features to code the CBT sessions automatically. We investigate both word-level and utterance-level features and propose feature fusion strategies to combine them. The utterance level features include dialog act tags as well as behavioral codes drawn from another well-known talk psychotherapy called Motivational Interviewing (MI). We propose a novel method to augment the word-based features with the utterance level tags for subsequent CBT code estimation. Experiments show that our new fusion strategy outperforms all the studied features, both when used individually and when fused by direct concatenation. We also find that incorporating a sentence segmentation module can further improve the overall system given the preponderance of multi-utterance conversational turns in CBT sessions.

Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.

During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., 'displays warmth and confidence', or 'attempts to set up collaboration') to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS.

Changes in Community Clinicians' Attitudes and Competence following a Transdiagnostic Cognitive Behavioral Therapy Training.

BACKGROUND: Although the literature suggest that attitudes toward evidence-based practices (EBPs) are associated with provider use of EBPs, less is known about the association between attitudes and how competently EBPs are delivered. This study examined how initial attitudes and competence relate to improvements in attitudes and competence following EBP training. METHODS: Program evaluation data was collected during implementation of an EBP in a large community mental health network. Clinicians (N=891) received intensive training in cognitive behavioral therapy followed by six months of consultation. Attitudes were assessed using the Evidence-Based Practice Attitude Scale, and competence was assessed using the Cognitive Therapy Rating Scale. Data were analyzed by fitting three latent change score models to examine the relationship between changes in attitudes and competence across the training and within its two phases (workshop phase, consultation phase). RESULTS: Latent change models identified significant improvement in attitudes (Ms latent change≥1.03, SEs≤ 0.18, zs≥6.55, ps< .001) and competence (Ms latent change ≥14.16, SEs≤3.10, zs≥2.82, ps<.001) across the full training and in each phase. Higher pre-workshop attitudes predicted significantly greater change in competence in the workshop and across training (bs≥1.62, SEs≤0.90, z≥1.09, p<.04, ß≥0.10); however, contrary to our hypothesis, post-workshop attitudes did not significantly predict change in competence in consultation (b=1.62, SE=0.86, z=1.87, p=.06, ß=0.09). Change in attitudes and change in competence in the training period and within the two phases were not significantly correlated. CONCLUSIONS: Results indicate that pre-training attitudes about EBPs present a target for implementation interventions, given their relation to changes in both attitudes and competence throughout training. Following participation in in itial training workshops, other factors such as subjective norms, implementation culture, or system-level policy shifts may be more predictive of change in competence through consultation.

Variability in clinician intentions to implement specific cognitive-behavioral therapy components.

BACKGROUND: CBT comprises many discrete components that vary in complexity, but implementation and training efforts often approach CBT as a single entity. We examined variability in clinician intentions to use different structural and interventional components of CBT for three different clinical groups: clients receiving CBT, clients with depression, and clients with anxiety. METHODS: Clinicians (n = 107) trained in CBT completed a one-time electronic survey. Clinicians' intentions were measured using established item stems from social psychology adapted to examine intentions to use six specific CBT components: exposure therapy, cognitive restructuring, behavioral activation, planning homework, reviewing homework, and agenda-setting. RESULTS: Intentions were weakest, on average, for exposure. They were strongest, on average, for reviewing homework. A series of ANOVAs with Tukey's post-hoc tests revealed that participants intended to use exposure with clients receiving CBT (p = .015) and clients with anxiety (p < .001) significantly more than for clients with depression. Participants intended to use behavioral activation with clients with depression (p = .01) significantly more than for clients with anxiety. No other intentions to use CBT components differed among these three clinical populations. CONCLUSIONS: When studying determinants of CBT use and designing interventions to increase use, implementers should consider that different CBT components may require different implementation strategies. TRIAL REGISTRATION: Not applicable.

Characterizing the heterogeneity of clinician practice use in community mental health using latent profile analysis.

BACKGROUND: The behavioral health service provider population is highly heterogeneous. However, it is rarely treated as such within evidence-based practice implementation efforts. This study aimed to evaluate, as a proof of concept, the utility of latent profile analysis to identify distinct profiles of clinician practices in a large sample of youth-serving community mental health clinicians. This study also aimed to identify predictors of profile membership to inform implementation efforts. METHODS: Participants were 484 practicing clinicians (79.4% female, 45.7% White, M age = 37.1 years). As part of a larger survey, clinicians reported on their use of cognitive, behavioral, family, and psychodynamic treatment techniques with a representative client on their caseload. Latent profile analysis was used to determine the presence of clinician practice profiles. Multilevel multinomial logistic regressions examined predictors of profile membership. RESULTS: Latent profile analysis indicated a 4-profile solution best fit the data, with clinicians who: 1) used generally low levels of all examined techniques and preferred cognitive techniques (Low Eclectics, 16%), 2) delivered moderate levels of all techniques (Moderate Eclectics, 53%), 3) demonstrated preference for use of family techniques (Family Preferred, 11%), and 4) used high levels of all techniques (Super Users, 20%). Clinician discipline (e.g., social work), education, and years of experience predicted profile membership. CONCLUSIONS: Findings from this proof of concept study underscore the utility of latent profile analysis to characterize the complex and heterogeneous makeup of community mental health. Results extend prior work highlighting the eclectic nature of community mental health practice. Predictor analyses underscore the important influence of clinician background characteristics on practice use.

Leveraging routine clinical materials and mobile technology to assess CBT fidelity: the Innovative Methods to Assess Psychotherapy Practices (imAPP) study.

BACKGROUND: Identifying scalable strategies for assessing fidelity is a key challenge in implementation science. However, for psychosocial interventions, the existing, reliable ways to test treatment fidelity quality are often labor intensive, and less burdensome strategies may not reflect actual clinical practice. Cognitive behavioral therapies (CBTs) provide clinicians with a set of effective core elements to help treat a multitude of disorders, which, evidence suggests, need to be delivered with fidelity to maximize potential client impact. The current "gold standard" for rating CBTs is rating recordings of therapy sessions, which is extremely time-consuming and requires a substantial amount of initial training. Although CBTs can vary based on the target disorder, one common element employed in most CBTs is the use of worksheets to identify specific behaviors and thoughts that affect a client's ability to recover. The present study will develop and evaluate an innovative new approach to rate CBT fidelity, by developing a universal CBT scoring system based on worksheets completed in therapy sessions. METHODS: To develop a scoring system for CBT worksheets, we will compile common CBT elements from a variety of CBT worksheets for a range of psychiatric disorders and create adherence and competence measures. We will collect archival worksheets from past studies to test the scoring system and assess test-retest reliability. To evaluate whether CBT worksheet scoring accurately reflects clinician fidelity, we will recruit clinicians who are engaged in a CBT for depression, anxiety, and/or posttraumatic stress disorder. Clinicians and clients will transmit routine therapy materials produced in session (e.g., worksheets, clinical notes, session recordings) to the study team after each session. We will compare observer-rated fidelity, clinical notes, and fidelity-rated worksheets to identify the most effective and efficient method to assess clinician fidelity. Clients will also be randomly assigned to either complete the CBT worksheets on paper forms or on a mobile application (app) to learn if worksheet format influences clinician and client experience or differs in terms of reflecting fidelity. DISCUSSION: Scoring fidelity using CBT worksheets may allow clinics to test fidelity in a short and effective manner, enhancing continuous quality improvement in the workplace. Clinicians and clinics can use such data to improve clinician fidelity in real time, leading to improved patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT03479398 . Retrospectively registered March 20, 2018.

Clinical Case Consultation and Experiential Learning in Cognitive Behavioral Therapy Implementation: Brief Qualitative Investigation.

There has been an increased emphasis on the implementation of cognitive behavioral therapy (CBT) to community mental health (CMH) systems due to its broad efficacy. Previous research has highlighted the importance of ongoing consultation in this process. The current study clarifies the role and process of clinical case consultation in the implementation of CBT to CMH from the consultants' perspective. Trainers from two large-scale implementation initiatives (n = 27) were surveyed regarding their strategies used in the consultation process. Historically, researchers have focused on trainees who view relational variables as the most effective elements of consultation; however, in the current study, trainers perceived experiential learning as being the most effective consultation strategy for helping CMH clinicians learn CBT. Other aspects of consultation such as case conceptualization and practice sample review are discussed in terms of their relative perceived utility in raising CMH clinician's competency to treat comorbid patients.

Testing a Web-Based, Trained-Peer Model to Build Capacity for Evidence-Based Practices in Community Mental Health Systems.

OBJECTIVE: Use of expert-led workshops plus consultation has been established as an effective strategy for training community mental health (CMH) clinicians in evidence-based practices (EBPs). Because of high rates of staff turnover, this strategy inadequately addresses the need to maintain capacity to deliver EBPs. This study examined knowledge, competency, and retention outcomes of a two-phase model developed to build capacity for an EBP in CMH programs. METHODS: In the first phase, an initial training cohort in each CMH program participated in in-person workshops followed by expert-led consultation (in-person, expert-led [IPEL] phase) (N=214 clinicians). After this cohort completed training, new staff members participated in Web-based training (in place of in-person workshops), followed by peer-led consultation with the initial cohort (Web-based, trained-peer [WBTP] phase) (N=148). Tests of noninferiority assessed whether WBTP was not inferior to IPEL at increasing clinician cognitive-behavioral therapy (CBT) competency, as measured by the Cognitive Therapy Rating Scale. RESULTS: WBTP was not inferior to IPEL at developing clinician competency. Hierarchical linear models showed no significant differences in CBT knowledge acquisition between the two phases. Survival analyses indicated that WBTP trainees were less likely than IPEL trainees to complete training. In terms of time required from experts, WBTP required 8% of the resources of IPEL. CONCLUSIONS: After an initial investment to build in-house CBT expertise, CMH programs were able to use a WBTP model to broaden their own capacity for high-fidelity CBT. IPEL followed by WBTP offers an effective alternative to build EBP capacity in CMH programs, rather than reliance on external experts.

Implementation of transdiagnostic cognitive therapy in community behavioral health: The Beck Community Initiative.

OBJECTIVE: Progress bringing evidence-based practice (EBP) to community behavioral health (CBH) has been slow. This study investigated feasibility, acceptability, and fidelity outcomes of a program to implement transdiagnostic cognitive therapy (CT) across diverse CBH settings, in response to a policy shift toward EBP. METHOD: Clinicians (n = 348) from 30 CBH programs participated in workshops and 6 months of consultation. Clinician retention was examined to assess feasibility, and clinician feedback and attitudes were evaluated to assess implementation acceptability. Experts rated clinicians' work samples at baseline, mid-, and end-of-consultation with the Cognitive Therapy Rating Scale (CTRS) to assess fidelity. RESULTS: Feasibility was demonstrated through high program retention (i.e., only 4.9% of clinicians withdrew). Turnover of clinicians who participated was low (13.5%) compared to typical CBH turnover rates, even during the high-demand training period. Clinicians reported high acceptability of EBP and CT, and self-reported comfort using CT improved significantly over time. Most clinicians (79.6%) reached established benchmarks of CT competency by the final assessment point. Mixed-effects hierarchical linear models indicated that CTRS scores increased significantly from baseline to the competency assessment (p < .001), on average by 18.65 points. Outcomes did not vary significantly between settings (i.e., outpatient vs. other). CONCLUSIONS: Even clinicians motivated by policy-change rather than self-nomination may feasibly be trained to deliver a case-conceptualization driven EBP with high levels of competency and acceptability. (PsycINFO Database Record

Beyond the Label: Relationship Between Community Therapists' Self-Report of a Cognitive Behavioral Therapy Orientation and Observed Skills.

Policy-makers, payers, and consumers often make decisions based on therapists' reported theoretical orientations, but little is known about whether these labels represent actual or potential skills. Prior to CBT training, therapists (n = 321) reported theoretical orientations. Experts rated CBT competency using the Cognitive Therapy Rating Scale Therapy at pre-, mid-, and post-training. CBT- and non-CBT identified therapists showed equivalent, non-competent baseline CBT skills. CBT-identified therapists showed greater CBT skills at mid-training, but by end of training, groups evidenced equivalent achieved competency. Baseline CBT orientations were neither valid, nor useful markers of later competency. Policy, clinical and research implications are discussed.

Training and Consultation in Evidence-Based Psychosocial Treatments in Public Mental Health Settings: The ACCESS Model.

We present a model of training in evidence-based psychosocial treatments (EBTs). The ACCESS (assess and adapt, convey basics, consult, evaluate, study outcomes, sustain) model integrates principles and findings from adult education and training literatures, research, and practical suggestions based on a community-based clinician training program. Descriptions of the steps are provided as a means of guiding implementation efforts and facilitating training partnerships between public mental health agencies and practitioners of EBTs.

In-session exposure tasks and therapeutic alliance across the treatment of childhood anxiety disorders.

The study examined the shape of therapeutic alliance using latent growth curve modeling and data from multiple informants (therapist, child, mother, father). Children (n = 86) with anxiety disorders were randomized to family-based cognitive-behavioral treatment (FCBT; N = 47) with exposure tasks or to family education, support, and attention (FESA; N = 39). Children in FCBT engaged in exposure tasks in Sessions 9-16, whereas FESA participants did not. Alliance growth curves of FCBT and FESA youths were compared to examine the impact of exposure tasks on the shape of the alliance (between-subjects). Within FCBT, the shape of alliance prior to exposure tasks was compared with the shape of alliance following exposure tasks (within-subjects). Therapist, child, mother, and father alliance ratings indicated significant growth in the alliance across treatment sessions. Initial alliance growth was steep and subsequently slowed over time, regardless of the use of exposure tasks. Data did not indicate a rupture in the therapeutic alliance following the introduction of in-session exposures. Results are discussed in relation to the processes, mediators, and ingredients of efficacious interventions as well as in terms of the dissemination of empirically supported treatments.

Pediatric behavior management techniques: a survey of predoctoral dental students.

Dental schools are increasingly incorporating behavioral management strategies into the curriculum; however, little is known about the efficacy of this instruction. The purposes of this study were to evaluate student exposure to several categories of behavioral management techniques, assess student opportunity to observe faculty use of these techniques, and determine the extent of students' personal use of various behavioral management techniques. Third-year dental students (n=98, X age=26.52; s=4.05) were administered a survey assessing their exposure to and willingness to use behavioral management strategies. Results indicated differences between the techniques students recalled being taught and what they indicated they plan to use in their own future clinical practices. Student technique endorsement also varied as a function of student age, gender, ethnicity, and patient age. Despite increasing concerns regarding the use of these techniques, a significant minority of students stated that they were taught to use hand-over-mouth, verbal intimidation, and various forms of active and passive restraint/immobilization. While appreciation for behavioral management strategies within the dental school curriculum was demonstrated by the amount of didactic exposure students received, the need for increased experiential training is evident. Furthermore, student endorsement of controversial techniques appears to reflect the changing view of these techniques within the professional dental community.