Objective: The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), developed for the World Health Organization (WHO), screens for risks associated with the use of tobacco, alcohol, and seven categories of drugs. Although the ASSIST has acceptable psychometric properties, it is relatively long for a screening test. This study was designed to identify a subset of questions from the full ASSIST instrument having comparable psychometric properties for the classification of low-, moderate-, and high-risk substance use.
Method: The study used three data sets from prior studies using the WHO ASSIST. Samples 1 and 3 were obtained from WHO multisite studies conducted in seven countries. Sample 2 included patient data from a U.S.-based screening and brief intervention program that incorporated the ASSIST into its clinical protocol. Samples 1 and 2 were used to conduct psychometric analyses for combinations of ASSIST items. Sample 3 was used to estimate sensitivity, specificity, and positive and negative predictive value for a two-item ASSIST.
Results: Based on correlation statistics, reliability metrics, and validation analyses, a new, two-item version is proposed. The ASSIST-FC contains one question about the frequency (F) of current use and a second question about current or past concern (C) expressed by others. The ASSIST-FC demonstrates no substantial loss in reliability, validity, and predictive ability when statistically compared with the full-length ASSIST.
Conclusions: The ASSIST-FC has advantages for clinical applications in settings where a brief, efficient, reliable screening test is needed to identify patients with hazardous and harmful substance use who would benefit from a brief intervention. It can also be used to identify patients who are manifesting symptoms of substance dependence that would require further diagnostic evaluation.
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J Med Internet Res
January 2025
Cancer Screening, American Cancer Society, Atlanta, GA, United States.
Background: The online nature of decision aids (DAs) and related e-tools supporting women's decision-making regarding breast cancer screening (BCS) through mammography may facilitate broader access, making them a valuable addition to BCS programs.
Objective: This systematic review and meta-analysis aims to evaluate the scientific evidence on the impacts of these e-tools and to provide a comprehensive assessment of the factors associated with their increased utility and efficacy.
Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFJCO Clin Cancer Inform
January 2025
Department of Radiology, Dr BRAIRCH, All India Institute of Medical Sciences, New Delhi, India.
Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports.
Materials And Methods: This study was approved by the Institute Ethics Committee. In phase I, five state-of-the-art LLMs (Generative Pre-Trained Transformer-4o [GPT-4o], Google Gemini, Claude Opus, Llama-3.
Am J Respir Crit Care Med
January 2025
University of Washington, Global Health, Seattle, Washington, United States.
Clin Infect Dis
January 2025
IQVIA Inc., Falls Church, VA.
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