Why are some cases not on track? An item analysis of the Assessment for Signal Cases during inpatient psychotherapy

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach

To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work sought to shorten and validate the 30-item Mandarin Dyslexia Screening Behavior Checklist for Primary School Students (the full checklist; Fan et al., , 19, 521-527, 2021). Our participants were 15,522 Mandarin-Chinese-speaking students and their parents, sampled from classrooms in grades 2-6 across regions in mainland China. A machine learning approach (lasso regression) was applied to shorten the full checklist (Fan et al., , 19, 521-527, 2021), constructing grade-specific brief checklists first, followed by a compilation of the common brief checklist based on the similarity across grade-specific checklists. All checklists (the full, grade-specific brief, and common brief versions) were validated and compared with data in our sample and an external sample (N = 114; Fan et al., , 19, 521-527, 2021). The results indicated that the six-item common brief checklist showed consistently high reliability (αs > .82) and reasonable classification performance (about 60% prediction accuracy and 70% sensitivity), comparable to that of the full checklist and all grade-specific brief checklists across our current sample and the external sample from Fan et al., , 19, 521-527, (2021). Our analysis showed that 2.42 (out of 5) was the cutoff score that helped classify children's reading status (children who scored higher than 2.42 might be considered at risk for dyslexia). Our final product is a valid, accessible, common brief checklist for prescreening primary school children at risk for Chinese dyslexia, which can be used across grades and regions in mainland China.

Systematic review of machine learning utilization within outpatient psychodynamic psychotherapy research

Introduction

Although outpatient psychodynamic psychotherapy is effective, there has been no improvement in treatment success in recent years. One way to improve psychodynamic treatment could be the use of machine learning to design treatments tailored to the individual patient's needs. In the context of psychotherapy, machine learning refers mainly to various statistical methods, which aim to predict outcomes (e.g., drop-out) of future patients as accurately as possible. We therefore searched various literature for all studies using machine learning in outpatient psychodynamic psychotherapy research to identify current trends and objectives.

Methods

For this systematic review, we applied the Preferred Reporting Items for systematic Reviews and Meta-Analyses Guidelines.

Results

In total, we found four studies that used machine learning in outpatient psychodynamic psychotherapy research. Three of these studies were published between 2019 and 2021.

Discussion

We conclude that machine learning has only recently made its way into outpatient psychodynamic psychotherapy research and researchers might not yet be aware of its possible uses. Therefore, we have listed a variety of perspectives on how machine learning could be used to increase treatment success of psychodynamic psychotherapies. In doing so, we hope to give new impetus to outpatient psychodynamic psychotherapy research on how to use machine learning to address previously unsolved problems.

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre-post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose-response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Depression Anxiety Stress Scale-10: A Brief Measure for Routine Psychotherapy Outcome and Progress Assessment

Routine outcome measurement and progress monitoring is well established to enhance quality assurance in clinical psychology service delivery but is not widely used in routine care. A major barrier to more widespread implementation is the lack of public domain, brief, psychometrically sound outcome measures that easily integrate into clinical information systems. The current study assessed a brief 10-item version of the widely used Depression Anxiety Stress (DASS)-42 scale, which we called the Depression Anxiety Stress-10 (DASS-10) scale. In two clinical samples of adults (n = 1036, 445 men, 591 women; and n = 1084, 493 men, 591 women), the DASS-10 had a replicable two-level factor structure, which at the lower level had two factors assessing stress-anxiety and depression, which each loaded onto a superordinate psychological distress scale. The items in the distress score discriminated between a clinical sample (n = 376) and a community sample (n = 379) and were sensitive to clinical change. The measure has the potential to make routine outcome measurement and progress monitoring more cost-effective to implement than existing measures, particularly when integrated with practice management software to make administration, scoring, and use easy.

The influence of extra-therapeutic social support on the association between therapeutic bond and treatment outcome

Objective: Both good therapeutic bond as well as extra-therapeutic social support seem to enhance treatment outcomes. Some features of the therapeutic bond are similar to experiences in extra-therapeutic relationships (e.g., feelings of trust or belongingness). Patients with a lack of social support might benefit particularly from a good therapeutic bond, because a well-formed bond can partly substitute relationship needs. This study replicates former research (main effects of bond and social support) and investigates the hypothesized interaction between both constructs. Method: Data from 1206 adult patients receiving cognitive-behavioral outpatient therapy were analyzed. Patients rated early therapeutic bond, their impairment, as well as their social support. Multilevel regression analyses were applied to test for main effects and interactions between bond and social support predicting therapy outcome post treatment. Results: Consistent with prior research, both therapeutic bond and social support predicted therapy outcome. Among patients with high social support, the impact of the therapeutic bond was minimal, while patients with low social support benefited most from a good therapeutic bond. Conclusions: Results suggest that both the therapeutic bond and social support play a role in therapy outcomes and that good therapeutic bond quality might be especially important if a patient lacks social support.

Why are some cases not on track? An item analysis of the Assessment for Signal Cases during inpatient psychotherapy

Within the Routine Outcome Monitoring system “OQ‐Analyst,” the questionnaire “Assessment for Signal Cases” (ASC) supports therapists in detecting potential reasons for not‐on‐track trajectories. Factor analysis and a machine learning algorithm (LASSO with 10‐fold cross‐validation) were applied, and potential predictors of not‐on‐track classifications were tested using logistic multilevel modeling methods. The factor analysis revealed a shortened (30 items) version of the ASC with good internal consistency (α = 0.72–0.89) and excellent predictive value (area under the curve = 0.98; positive predictive value = 0.95; negative predictive value = 0.94). Item‐level analyses showed that interpersonal problems captured by specific ASC items (not feeling able to speak about problems with family members; feeling rejected or betrayed) are the most important predictors of not‐on‐track trajectories. It should be considered that our results are based on analyses of ASC items only. Our findings need to be replicated in future studies including other potential predictors of not‐on‐track trajectories (e.g., changes in medication, specific therapeutic techniques, or treatment adherence), which were not measured this study.