Incremental Clinical Utility of ADHD Assessment Measures With Latino Families

Objective: This study examined the incremental clinical utility of parent and teacher reports of ADHD symptomatology and functional impairment in Latino youth, as well as parent and teacher agreement with the final clinical judgment on a diagnostic structured interview. Method: Participants included 70 Latino youth (47 males, 23 females; M age = 8.13 years, SD = 2.51 years) and their parents and teachers; 60 participants were diagnosed with ADHD. Correlations, percent agreement, kappas, and regressions were utilized. Results: Results demonstrated that teachers agreed with the final clinical judgment more often than did parents. Results additionally demonstrated that functional impairment did not statistically significantly improve diagnostic models already including ADHD symptoms; follow-up analyses were run and are discussed. Finally, results demonstrated that teacher reports statistically significantly improved diagnostic models already including parent reports. Conclusion: The current findings suggest the importance of including both parent and teacher reports of both ADHD symptomatology and functional impairment when assessing ADHD in Latino youth

Incremental Clinical Utility of ADHD Assessment Measures with Latino Families

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common disorder beginning in childhood, with related symptoms and impairment across settings often persisting into adolescence and adulthood if effective treatment is not provided (Bernardi et al., 2012). Therefore, the early and accurate assessment and diagnosis of ADHD is critical. While the prevalence of ADHD symptomatology has been found to be consistent between Latinos and European Americans (Morgan, Hillemeir, Farkas, & Maczuga, 2014), there is little research on the best practices for assessing ADHD in Latinos. The current study sought to examine the incremental clinical utility of two parent- and teacher-report measures of ADHD symptomatology and functional impairment used to assess ADHD in a sample of Latino children. A sample of Latino schoolchildren (N=53) was recruited to participate in the current study, along with their primary parents and teachers; a comprehensive ADHD assessment was conducted for each participant. Results suggest that teachers in the current sample had a higher rate of agreement with final clinical judgment than did parents in the current sample. Additionally, results suggest that parent- and teacher-reports of functional impairment did not add incremental utility in predicting ADHD diagnostic status, beyond that of parent- and teacher-reports of ADHD symptomatology; follow-up analyses suggest why this may be the case. Lastly, results suggest that teacher-reports of ADHD symptoms and functional impairment added incremental utility in predicting ADHD diagnostic status, beyond parent-reports of ADHD symptoms and functional impairment. Clinical implications of these findings will be discussed

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England

Exploring the Effectiveness of AI Algorithms in Predicting and Enhancing Student Engagement in an E-Learning

The shift from traditional to digital learning platforms has highlighted the need for more personalized and engaging student experiences. In response, researchers are investigating AI algorithms' ability to predict and improve e-learning student engagement.  Machine Learning (ML) methods like Decision Trees, Support Vector Machines, and Deep Learning models can predict student engagement using variables like interaction patterns, learning behavior, and academic performance. These AI algorithms have identified at-risk students, enabling early interventions and personalized learning. By providing adaptive content, personalized feedback, and immersive learning environments, some AI methods have increased student engagement. Despite these advances, data privacy, unstructured data, and transparent and interpretable models remain challenges. The review concludes that AI has great potential to improve e-learning outcomes, but these challenges must be addressed for ethical and effective applications. Future research should develop more robust and interpretable AI models, multidimensional engagement metrics, and more comprehensive studies on AI's ethical implications in education

NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS

Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts with employers identifying the particular skills required for a role, and then screening and evaluating candidates’ competencies against those requirements. With the recent rise in employers adopting skills-based hiring practices, it has become integral for students to take courses that improve their marketability and support their long-term career success. A 2017 survey of over 32,000 students at 43 randomly selected institutions found that only 34% of students believe they will graduate with the skills and knowledge required to be successful in the job market. Furthermore, the study found that while 96% of chief academic officers believe that their institutions are very or somewhat effective at preparing students for the workforce, only 11% of business leaders strongly agree [11]. An implication of the misalignment is that college graduates lack the skills that companies need and value. Fortunately, the rise of skills-based hiring provides an opportunity for universities and students to establish and follow clearer classroom-to-career pathways. To this end, this paper presents a course recommender system that aims to improve students’ career readiness by suggesting relevant skills and courses based on their unique career interests

Thai secondary school science classrooms: Constructivist learning environments

This paper describes the first study conducted in Thailand (2002-2003) that resulted in changes in science teachers’ classroom environments. In the first phase of the study, the Constructivist Learning Environment Survey (CLES), an instrument for assessing students’ perceptions of the actual and preferred classroom environment through the constructivist perspective, was validated for use in Thailand. Second, typical Thai secondary school science classroom environments were described using quantitative and qualitative methods. Finally, the effectiveness of constructivist teaching in promoting improvement in classroom environments was evaluated through an action research process, involving the use of feedback on actual and preferred classroom environments. The sample consisted of seven secondary science teachers and their 17 classes of 606 students in Nakornsawan Province, Thailand. Student Actual and Preferred Forms of the CLES, assessing Personal Relevance, Uncertainty, Critical Voice, Shared Control and Student Negotiation, were administered. Factor analysis and internal consistency measures supported a five-factor structure for both actual and preferred forms. Students’ attitudes to science were also measured. The actual and preferred environments of different classes were described based on profiles of classroom environment scores. A number of teachers then participated in an attempt to improve their classroom environments, through the use of a constructivist teaching approach. Changes in classrooms did occur, thus supporting the effectiveness of constructivist teaching in improving learning environments and students’ attitudes towards science in Thailand

Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence

Generalized anxiety disorder (GAD) and major depressive disorder (MDD) are highly prevalent and impairing problems, but frequently go undetected, leading to substantial treatment delays. Electronic health records (EHRs) collect a great deal of biometric markers and patient characteristics that could foster the detection of GAD and MDD in primary care settings. We approached the problem of predicting MDD and GAD using a novel machine learning pipeline to re-analyze data from an observational study. The pipeline constitutes an ensemble of algorithmically distinct machine learning methods, including deep learning. A sample of 4,184 undergraduate students completed the study, undergoing a general health screening and completing a psychiatric assessment for MDD and GAD. After explicitly excluding all psychiatric information, 59 biomedical and demographic features from the general health survey in addition to a set of engineered features were used for model training. We assessed the model\u27s performance on a held-out test set and found an AUC of 0.73 (sensitivity: 0.66, specificity: 0.7) and 0.67 (sensitivity: 0.55, specificity: 0.7) for GAD, and MDD, respectively. Additionally, we used advanced techniques (SHAP values) to illuminate which features had the greatest impact on prediction for each disease. The top predictive features for MDD were being satisfied with living conditions and having public health insurance. The top predictive features for GAD were vaccinations being up to date and marijuana use. Our results indicate moderate predictive performance for the application of machine learning methods in detection of GAD and MDD based on EHR data. By identifying important predictors of GAD and MDD, these results may be used in future research to aid in the early detection of MDD and GAD