Six month durability of targeted cognitive training supplemented with social cognition exercises in schizophrenia.

Background:Deficits in cognition, social cognition, and motivation are significant predictors of poor functional outcomes in schizophrenia. Evidence of durable benefit following social cognitive training is limited. We previously reported the effects of 70 h of targeted cognitive training supplemented with social cognitive exercises (TCT + SCT) verses targeted cognitive training alone (TCT). Here, we report the effects six months after training. Methods:111 participants with schizophrenia spectrum disorders were randomly assigned to TCT + SCT or TCT-only. Six months after training, thirty-four subjects (18 TCT + SCT, 16 TCT-only) were assessed on cognition, social cognition, reward processing, symptoms, and functioning. Intent to treat analyses was used to test the durability of gains, and the association of gains with improvements in functioning and reward processing were tested. Results:Both groups showed durable improvements in multiple cognitive domains, symptoms, and functional capacity. Gains in global cognition were significantly associated with gains in functional capacity. In the TCT + SCT group, participants showed durable improvements in prosody identification and reward processing, relative to the TCT-only group. Gains in reward processing in the TCT + SCT group were significantly associated with improvements in social functioning. Conclusions:Both TCT + SCT and TCT-only result in durable improvements in cognition, symptoms, and functional capacity six months post-intervention. Supplementing TCT with social cognitive training offers greater and enduring benefits in prosody identification and reward processing. These results suggest that novel cognitive training approaches that integrate social cognitive exercises may lead to greater improvements in reward processing and functioning in individuals with schizophrenia

‘Is education the making mind full, or is it the making mind strong?' Reflections on the 1994 history workshop teachers' conference

Paper presented at the Wits History Workshop: Democracy, Popular Precedents, Practice and Culture, 13-15 July, 199

Evaluating the Effects of MINDSTRONG™ in Graduate Nursing Students

Abstract Problem A high rate of burnout and lack of resiliency is a major problem in nursing graduate students resulting in dropout and mental health issues. MINDSTRONG™ is an evidence-based cognitive behavioral skills-building program with the goal of building resiliency and decreasing burnout through coping skills to improve overall adult health and well-being. Methods A descriptive design using quantitative data analysis through pre, and post surveys was used. The surveys evaluated graduate nursing students’ burnout and resiliency after participating in MINDSTRONG™, a cognitive behavioral theory program, implemented by trained facilitators for seven sessions. The sample consisted of all six self-enrolled graduate nursing students recruited through university emails in a mid-sized public university located in Midwest. Results: Participants receiving MINDSTRONG™ cognitive-based interventions reported slightly less burnout and minimal resiliency improvement. Implications for Practice:  The MINDSTRONG™ program can be used as a preventive and early intervention for improving mental health outcomes and lifestyle behaviors in graduate students if required as a credit-based class

An Evaluation of MINDSTRONG™ Implementation with Graduate Nursing Students

Problem: Nursing graduate students are at increased risk of greater stress, anxiety, and depression (Hoying, 2020; Melnyk et al., 2020). Cognitive Behavioral Therapy is the gold standard in the treatment of anxiety and depression (Hoying et al., 2020; Melnyk et al., 2015; Melnyk et al., 2020). MINDSTRONG™, a CBT-based training program, has been proven in many studies to be effective in helping individuals prevent or cope with these issues (The Ohio State University College of Nursing, 2020). Methods: This Quality Improvement (QI) project was a descriptive-observational, pre-post design. Sample and setting were nursing graduate students from a Midwestern, middle-sized urban, public university. Descriptive statistics and the Wilcoxon signed-rank tests were used to compare pre-and post-intervention results. Results: The sample for this project consisted of six graduate nursing students. The results indicate no statistically significant difference in pre- post Perceived Stress Scale (PSS) and Generalized Anxiety Disorder-7 (GAD-7) scores, though results were clinically significant, with 83.3% (n = 5, N = 6) participants with improved stress and anxiety. There was a statistically significant difference in pre-post Patient Health Questionnaire-8 (PHQ-8) scores with a p = .043. Overall, 83.3% (n = 5) of participants had decreased depression symptoms with the two participants rated with ‘severe’ depression scores having the greatest improvement. Discussion: Though the sample size was small, the results in this QI project are consistent with that of other studies on the MINDSTRONG™ program. This QI project supports the continued use of MINDSTRONG™ to improve the mental health of graduate nursing students

Evidence to support routine mental health screening and intervention in graduate health sciences students

The prevalence of depression is growing in the United States and suicide is second leading cause of death among people ages 15-34. Providing screening and using APRN students as wellness coaches to deliver an evidence-based cognitive-behavioral skills building program can increase the health and well-being of other health sciences\u27 students

Enhancing Mental Health with Artificial Intelligence: Current Trends and Future Prospects

Artificial Intelligence (AI) has emerged as a transformative force in various fields, and its application in mental healthcare is no exception. Hence, this review explores the integration of AI into mental healthcare, elucidating current trends, ethical considerations, and future directions in this dynamic field. This review encompassed recent studies, examples of AI applications, and ethical considerations shaping the field. Additionally, regulatory frameworks and trends in research and development were analyzed. We comprehensively searched four databases (PubMed, IEEE Xplore, PsycINFO, and Google Scholar). The inclusion criteria were papers published in peer-reviewed journals, conference proceedings, or reputable online databases, papers that specifically focus on the application of AI in the field of mental healthcare, and review papers that offer a comprehensive overview, analysis, or integration of existing literature published in the English language. Current trends reveal AI's transformative potential, with applications such as the early detection of mental health disorders, personalized treatment plans, and AI-driven virtual therapists. However, these advancements are accompanied by ethical challenges concerning privacy, bias mitigation, and the preservation of the human element in therapy. Future directions emphasize the need for clear regulatory frameworks, transparent validation of AI models, and continuous research and development efforts. Integrating AI into mental healthcare and mental health therapy represents a promising frontier in healthcare. While AI holds the potential to revolutionize mental healthcare, responsible and ethical implementation is essential. By addressing current challenges and shaping future directions thoughtfully, we may effectively utilize the potential of AI to enhance the accessibility, efficacy, and ethicality of mental healthcare, thereby helping both individuals and communities

The AURORA Study: A Longitudinal, Multimodal Library of Brain Biology and Function after Traumatic Stress Exposure

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions