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College student mental health has been a critical concern for professional counselors. Anxiety and depressive disorders have become increasingly prevalent over the past decade. Utilizing machine learning, a subset of artificial intelligence (AI), we developed predictive models (i.e., eXtreme Gradient Boosting [XGBoost], Random Forest, Decision Tree, and Logistic Regression) to identify US college students at heightened risk of diagnosable anxiety and depressive disorders. The dataset included 61,619 students from 133 US higher education institutions and was partitioned into a 90:10 ratio for training and testing the models. We employed hyperparameter tuning and cross-validation to optimize model performance and examined multiple measures of predictive performance (e.g., area under the receiver operating characteristic curve [AUC], accuracy, sensitivity). Results revealed strong discriminative power in our machine learning predictive models with AUC of 0.74 and 0.77, indicating current financial situation, sense of belonging on campus, disability status, and age as the top predictors of anxiety and depressive disorders. This study provides a practical tool for professional counselors to proactively identify students for anxiety and depressive disorders before these conditions escalate. Application of machine learning in counseling research provides data-driven insights that help enhance the understanding of mental health determinants, guide prevention and intervention strategies, and promote the well-being of diverse student populations through counseling.
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Parents and caregivers of individuals with autism spectrum disorder (ASD) are faced with unique challenges and stressors from the early stages of their children’s development, through subsequent diagnosis, navigating the educational and therapeutic landscapes, and into adulthood. Whether their child requires Level 1, 2, or 3 supports according to their Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition diagnosis, parents will be faced with many different types of stressors. Given their experiences and knowledge of their children, they can offer valuable insights to educators and service providers that inform interventions and lead to optimal outcomes for individuals with ASD. This chapter explores the common challenges encountered by parents and caregivers and offers suggestions for ways that professional educators can best engage with them so as to establish optimal supports for individuals with autism. © 2024 Taylor & Francis Group. All rights reserved.
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This chapter briefly explores the history and development of teaming in education. The three most common models of educational teaming are defined and discussed in detail. The primary focus of the chapter is on the Individuals with Disabilities Education Act-mandated multidisciplinary team and how it can be transformed into a transdisciplinary teaming model in a school setting that serves individuals with autism spectrum disorder (ASD). This transdisciplinary teaming model is student centric and stresses the importance of considering the whole child, specifically as it relates to the team’s systematic communication, goal sharing, knowledge of objectives and agenda items, and the need for ongoing clarification of knowledge among the team. The emphasis is on shared knowledge that leads to greater success of the team in meeting the needs of the student under discussion. This transdisciplinary teaming model also serves as a means to troubleshoot student challenges through a group problem-solving process. The six elements in the development of a transdisciplinary teaming model are discussed in detail. © 2024 Taylor & Francis Group. All rights reserved.
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Counselor educators and supervisors have a vital role in promoting legislative professional advocacy (LPA). An interpretative qualitative analysis study of counseling graduate students involved in state-level LPA was conducted. Findings revealed six distinct themes: (1) professional counselor identity, (2) knowledge, attitudes and skills, (3) professional support, (4) power of the group, (5) social justice, and (6) personal impact. Study findings may help counselor educators and supervisors educate, guide, and mentor students, thereby increasing the likelihood of professional counselor LPA. © 2024 Chi Sigma Iota.
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