Psychological Assessment最新文献

Cognitive disengagement syndrome (CDS), formerly termed sluggish cognitive tempo, is a set of symptoms characterized by excessive daydreaming, mental confusion, and slowed behavior/thinking. CDS is distinct from symptoms of attention-deficit/hyperactivity disorder (ADHD) and other psychopathologies and uniquely associated with functional impairment. However, despite significant progress in developing and evaluating rating scale measures of CDS, no clinical interview of CDS exists with established psychometric properties that can facilitate a multimethod approach assessing CDS. Accordingly, the present study evaluated the psychometric properties of the semistructured Cognitive Disengagement Syndrome-Clinical Interview (CDS-CI). The CDS-CI and the ADHD inattention (ADHD-IN) and hyperactive-impulsivity (ADHD-HI) modules from the Kiddie Schedule of Affective Disorders and Schizophrenia for School-Age Children (K-SADS) were administered to a sample of early adolescents (N = 341, ages 10-12 years) and their caregivers. Adolescents and caregivers also completed rating scale measures of CDS and ADHD symptoms. The CDS-CI demonstrated high internal consistency and interrater reliability. CDS-CI scores showed excellent same-source discriminant validity from K-SADS, ADHD-IN, and ADHD-HI scores and encouraging convergent and discriminant validity with rating scale measures, especially for caregivers. Above and beyond K-SADS, ADHD-IN, and ADHD-HI scores, higher parent and adolescent CDS-CI scores were both significantly associated with greater functional impairment. A threshold of ≥ 6 out of 15 symptoms was selected as an initial threshold for determining clinically elevated CDS using the CDS-CI. Findings provide psychometric support for the CDS-CI as a tool in the multimethod assessment of CDS. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Progress monitoring is integral to evidence-based practice. Correctional settings, especially the supervision of individuals who commit sexual offenses, elicit public concern; negative outcomes can be catastrophic. Using a prospective longitudinal study of 2,939 men with a history of sexual offenses undergoing community supervision, we examined different models of progress monitoring and how they should inform the assessment of risk for sexual recidivism. We found that the most recent assessment scores of the ACUTE-2007 and STABLE-2007 sexual recidivism risk tools provided the best information about reoffending risk compared to using (a) the worst period of adjustments (i.e., highest risk score), (b) the best period of adjustments (i.e., lowest risk score), or (c) a rolling average of scores. We also found that the latest STABLE-2007 scores incrementally predicted sexual recidivism beyond baseline risk as assessed by demographic and criminal history variables (Static-99R). We conclude that the risk for sexual recidivism changes over time and that community corrections is advanced by repeated assessment of dynamic (changeable) risk factors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

This article illustrates novel quantitative methods to estimate classification consistency in machine learning models used for screening measures. Screening measures are used in psychology and medicine to classify individuals into diagnostic classifications. In addition to achieving high accuracy, it is ideal for the screening process to have high classification consistency, which means that respondents would be classified into the same group every time if the assessment was repeated. Although machine learning models are increasingly being used to predict a screening classification based on individual item responses, methods to describe the classification consistency of machine learning models have not yet been developed. This article addresses this gap by describing methods to estimate classification inconsistency in machine learning models arising from two different sources: sampling error during model fitting and measurement error in the item responses. These methods use data resampling techniques such as the bootstrap and Monte Carlo sampling. These methods are illustrated using three empirical examples predicting a health condition/diagnosis from item responses. R code is provided to facilitate the implementation of the methods. This article highlights the importance of considering classification consistency alongside accuracy when studying screening measures and provides the tools and guidance necessary for applied researchers to obtain classification consistency indices in their machine learning research on diagnostic assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).