The prevalence of bullying has increased over the past decade and is a public health concern. Existing measures of bullying lack breadth and/or psychometric support, which may inaccurately represent current rates of bullying. The purpose of this study was to develop and validate a psychometrically sound measure, the Multidimensional Bullying Victimization Scale (MBVS), to evaluate bullying in adolescents. An initial pool of items was generated and rated by 600 adolescents. Exploratory factor analyses yielded three factors—direct bulling, indirect bullying, and evaluative bullying. Confirmatory factor analyses were conducted on independent sample of 623 adolescents, supporting the original three-factor solution. Results indicate that the MBVS demonstrates good internal consistency, construct validity, and concurrent validity. The MBVS scores were significantly and positively correlated with internalizing and externalizing symptoms. The MBVS provides a reliable and valid assessment of adolescents’ experiences with bully victimization.

Young adulthood is characterized by important life transitions (e.g., college, employment, relocation, marriage), where time management skills and routines help promote positive adjustment. Routines are observable, repetitive behavior that are context specific and automate aspects of daily life (e.g., personal hygiene, health, occupational, academic). Although measures of routines exist for children, adolescents, and older adults, similar measures assessing young adult routines are lacking. The purpose of this study was to develop and initially validate The Young Adult Routines Inventory (YARI). Analyses revealed a four-factor measure reflecting daily routines, social routines, time management, and procrastination. The YARI demonstrates good internal consistency, construct, and convergent validity, and was positively correlated with measures of emotional well-being and perceived life satisfaction. The YARI was negatively correlated with self-reported symptoms of Attention-Deficit/Hyperactivity Disorder (ADHD) and successfully distinguished individuals with and without ADHD symptomatology. Preliminary evidence suggests the YARI is a promising measure of young adult routines.

The quality of parent–child interaction is critical for child cognitive development. The Dyadic Parent–Child Interaction Coding System (DPICS) is commonly used to assess parent and child behaviors. However, manual annotation of DPICS codes by parent–child interaction therapists is a time-consuming task. To assist therapists in the coding task, researchers have begun to explore the use of artificial intelligence in natural language processing to classify DPICS codes automatically. In this study, we utilized datasets from the DPICS book manual, five families, and an open-source PCIT dataset. To train DPICS code classifiers, we employed the pre-trained fine-tuned model RoBERTa as our learning algorithm. Our study shows that fine-tuning the pre-trained RoBERTa model achieves the highest results compared to other methods in sentence-based DPICS code classification assignments. For the DPICS manual dataset, the overall accuracy was 72.3% (72.2% macro-precision, 70.5% macro-recall, and 69.6% macro-F-score). Meanwhile, for the PCIT dataset, the overall accuracy was 79.8% (80.4% macro-precision, 79.7% macro-recall, and 79.8% macro-F-score), surpassing the previous highest results of 78.3% accuracy (79% precision, 77% recall) averaged over the eight DPICS classes. These results show that fine-tuning the pre-trained RoBERTa model could provide valuable assistance to experts in the labeling process.