Interpersonal counselling for adolescent depression delivered by youth mental health workers without core professional training: the ICALM feasibility RCT by Jon Wilson, Viktoria Cestaro, Eirini Charami-Roupa, Timothy Clarke, Aoife Dunne, Brioney Gee, Sharon Jarrett, Thando Katangwe-Chigamba, Andrew Laphan, Susie McIvor, Richard Meiser-Stedman, Jamie Murdoch, Thomas Rhodes, Carys Seeley, Lee Shepstone, David Turner, Paul Wilkinson

“…Further work is needed to improve the feasibility of conducting such trials before they are attempted in future. …”
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Contextual Vulnerability Should Guide Fair Subject Selection in Xenotransplantation Clinical Trials by Gianna Strand

“…[xlvii] Potential subjects enrolled in upcoming xenograft research must be selected for their ability to answer the scientific objectives of a proposed study and must have the capacity to provide freely given informed consent within a fair research environment. …”
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Providing a development model for the effectiveness of persuasive advertising in social networks by Hamzeh Hessani Khabr, Payam Paslari, Mehdi Bagheri, Saeed Muradpour

“…Finally, this research also answers the main question: How to provide a development model for the effectiveness of persuasive advertising in social networks?…”
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Consequences, costs and cost-effectiveness of workforce configurations in English acute hospitals by Peter Griffiths, Christina Saville, Jane Ball, David Culliford, Jeremy Jones, Francesca Lambert, Paul Meredith, Bruna Rubbo, Lesley Turner, Chiara Dall’Ora

“…Our study used existing data from national reports and daily staffing data from hospital wards to answer some of the main uncertainties from past research. …”
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Exploring voluntary sector specialist services for victim-survivors of sexual violence in England: the PROSPER co-production study by Caroline Bradbury-Jones, Sarah Damery, Kirsten Fruin, Clare Gunby, Jenny Harlock, Lucy Hebberts, Louise Isham, Anne-Marie Jones, Fay Maxted, Amelia Mighty, Priti Parmar, Laura Patterson, Jason Schaub, Fee Scott, Harriet Smailes, Debs Smith, Julie Taylor

“…This research tried to answer these questions by speaking with people who provide the funding, people who work in the services and people who use the services. …”
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Clinical and cost-effectiveness of detailed anomaly ultrasound screening in the first trimester: a mixed-methods study by Jehan N Karim, Helen Campbell, Pranav Pandya, Edward C F Wilson, Zarko Alfirevic, Trish Chudleigh, Elizabeth Duff, Jane Fisher, Hilary Goodman, Lisa Hinton, Christos Ioannou, Edmund Juszczak, Louise Linsell, Heather L Longworth, Kypros H Nicolaides, Anne Rhodes, Gordon Smith, Basky Thilaganathan, Jim Thornton, Gillian Yaz, Oliver Rivero-Arias, Aris T Papageorghiou

“…We conducted several studies to answer this question. First, we reviewed the experiences of hospitals who already offer this early scan. …”
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Towards Automated Error Analysis: Learning to Characterize Errors by Tong Gao, Shivang Singh, Raymond Mooney

“…We apply our approach to VilBERT, for Visual Question Answering, and RoBERTa, for Common Sense Question Answering. …”
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Assessment of the Difficulty Level of Science Questions Using the TIMSS Question Semantic Evaluation for Grade IV Elementary School Students by Muhammad Romli, Ida Bagus Putu Arnyana, I Wayan Suastra

“…The semantic difficulty level of TIMSS questions is determined based on the percentage of correct answers from students, and the question difficulty level is calculated using the percentage of correct answers from the entire student sample. …”
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The effectiveness of mindfulness-based therapy on working memory indices in patients with hypertension: a randomized clinical trial by Fatemeh Rabipour, Seyed Davood Hosseininasab, Arsalan Salari

“…At the end of the intervention in the post-test, the mean working memory indices of the intervention group (1-back, correct answers = 75.17, reaction time = 462.61; and 2-back, correct answers = 67, reaction time = 495.81) and the control group (1-back, correct answers=69/35, reaction time=617/75; and 2-back, correct answers=59/71, reaction time=663/56) were significantly different (P<0.0001). …”
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An Interactive Framework for Personalized Navigation Based on Metacosmic Cultural Tourism and Large Model Fine-Tuning by Hang Guo, Zhiqiang Liu, Chuanqian Tang, Xiaodan Zhang

“…With the wide application of large language models (LLMs) and the rapid growth of metaverse tourism demand, the digital tour and personalized interaction of historical sites have become the key to improving users&#x2019; digital travel experience. …”
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A pilot study of the performance of Chat GPT and other large language models on a written final year periodontology exam by Shaun Ramlogan, Vidya Raman, Shayn Ramlogan

“…Chat GPT-4, guided by the exam parameters, generated answers as ‘Run 1’ and 6 months later as as ‘Run 2’. …”
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Analysis of factors influencing doctors' awareness of clinical guidelines for heart failure treatment by N. B. Perepech, A. V. Tregubov, I. E. Mikhailova

“…The mean number of correct answers to the questionnaire among cardiologists was significantly higher than among general practitioners (p&lt;0,001). …”
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Assessing ChatGPT-4’s performance on the US prosthodontic exam: impact of fine-tuning and contextual prompting vs. base knowledge, a cross-sectional study by Mahmood Dashti, Farshad Khosraviani, Tara Azimi, Delband Hefzi, Shohreh Ghasemi, Amir Fahimipour, Niusha Zare, Zohaib Khurshid, Syed Rashid Habib

“…The aim is to determine whether fine-tuning improves the AI’s accuracy in answering specialized questions. …”
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Artificial Intelligence-Based Large Language Models Can Facilitate Patient Education by Xochitl Bryson, BA, Marleni Albarran, BS, Nicole Pham, MPH, Arianne Salunga, DO, Taylor Johnson, MD, MPH, Grant D. Hogue, MD, Jaysson T. Brooks, MD, Kali R. Tileston, MD, Craig R. Louer, MD, Ron El-Hawary, MD, Meghan N. Imrie, MD, James F. Policy, MD, Daniel Bouton, MD, Arun R. Hariharan, MD, Sara Van Nortwick, MD, Vidyadhar V. Upasani, MD, Jennifer M. Bauer, MD, Andrew Tice, MD, John S. Vorhies, MD

“…AI and professional society websites were equally likely to generate answers with which the expert panel disagreed. Key Concepts: (1) Large language models (LLMs) are increasingly used for generating medical information online, necessitating an evaluation of their accuracy and effectiveness compared with traditional sources. (2) An expert panel of physicians compared artificial intelligence (AI)-generated answers with professional society–authored answers to pediatric scoliosis frequently asked questions, finding that both types of answers were equally favored in terms of accuracy and appropriateness. (3) The panel reported a similar rate of disagreement with AI-generated and professional society–generated answers, indicating that both had areas of controversy. (4) Over half of the expert panel members felt they could distinguish between AI-generated and professional society–generated answers but this did not relate to their preferences. (5) While AI can support medical information dissemination, further research and improvements are needed to address its limitations and ensure high-quality, accessible patient education. …”
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The Effect of Homogeneity Psycho Cognition Strategies on Students' Understanding of Physics Concepts in Static Fluid Topics by J. R. Batlolona, J. Jamaludin, A. Kalean

“…The analysis was carried out based on student answers to brief description questions and the level of student confidence in the accuracy of the answers. …”
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New Topological Approaches to Generalized Soft Rough Approximations with Medical Applications by Mostafa K. El-Bably, Muhammad I. Ali, El-Sayed A. Abo-Tabl

“…We explore some of their properties to improve existing techniques. A comparison has been made with some existing studies to show that accuracy measure of proposed technique shows an improvement. …”
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Some Problems of Political Socialization of the Youth in a Mirror of Empirical Research in a Mirror of Empirical Research by Ilija Viktorovich Strelnikov

“…Recommendations about improvement of educational policy with youth are formulated.…”
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The feasibility of using generative artificial intelligence for history taking in virtual patients by Yongjin Yi, Kyong-Jee Kim

“…The chatbot generated 96 pairs of questions and answers, totaling 1,325 words in 177 sentences. Discourse analysis of the scripts revealed that 2.6% (34) of the words generated by the chatbot were deemed implausible and were categorized into inarticulate answers, hallucinations, and missing important information. …”
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A Multimodal Deep Learning Approach for Legal English Learning in Intelligent Educational Systems by Yanlin Chen, Chenjia Huang, Shumiao Gao, Yifan Lyu, Xinyuan Chen, Shen Liu, Dat Bao, Chunli Lv

“…The user study further verified the system’s practical effectiveness in real teaching environments, with learners’ understanding improvement reaching 0.78, expression improvement reaching 0.75, and satisfaction score reaching 0.88, significantly outperforming traditional teaching methods and unimodal systems. …”
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BINGO! Elevating Medical Physiology Tutorials Through Gamification by Kelsey Temprine Grellinger, Hong Phan, Maria Sheakley, Gerrit J. Bouma, Alexandra Bayer, Biren A. Shah

“…Methods Each BINGO tutorial utilized a template featuring 50–60 questions aligned with prior learning events, was delivered to 90 second-year medical students, and lasted 50–80 minutes. Iterative improvements, including revealing correct answers and a polling system, were implemented based on student feedback. …”
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