98%
921
2 minutes
20
This study explores how the quality of brief dyadic written exchanges (lasting under 5 min) on a virtual platform and the nature of the conversational topic (abstract or concrete), influences physical, interpersonal, and psychological closeness between interlocutors. In the first experiment, participants engaged in written conversations on either an abstract or concrete topic under two conditions: (i) an interactive condition, where participants exchanged messages with another person, and (ii) a non-interactive condition, where participants wrote independently on the same topic, aware that another person was simultaneously doing the same. Results indicated that participants in the interactive condition reported feeling significantly closer to their interlocutor than those in the non-interactive condition. In addition, greater perceived pleasantness, intimacy, and the importance of the other person's contribution to the conversation were associated with increased feelings of closeness. However, inconclusive evidence was obtained regarding the interaction of the other person's contribution with the abstractness of the conversational topic during the written exchanges in fostering feelings of closeness. The second experiment focused only on the interactive condition, where we examined interpersonal dynamics across different subcategories of abstract (e.g., philosophical/spiritual, emotional, social, physical/spatio-temporal) and concrete topics (e.g., tools, animals, food). The results of the first experiment were replicated, reinforcing the idea that the quality of the virtual exchange-rather than the topic itself-plays a crucial role in fostering feelings of closeness between individuals.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869026 | PMC |
http://dx.doi.org/10.1016/j.heliyon.2025.e42526 | DOI Listing |
Alpha Psychiatry
August 2025
Information Sciences and Technology, George Mason University, Fairfax, VA 22030, USA.
Background: Herein, we report on the initial development, progress, and future plans for an autonomous artificial intelligence (AI) system designed to manage major depressive disorder (MDD). The system is a web-based, patient-facing conversational AI that collects medical history, provides presumed diagnosis, recommends treatment, and coordinates care for patients with MDD.
Methods: The system includes seven components, five of which are complete and two are in development.
JMIR Pediatr Parent
September 2025
Division of Prevention Science, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States.
Background: Alone time with health care providers is critical for adolescents, and several professional organizations recommend it. Alone time with providers promotes better utilization of health services, empowers adolescents to manage their health, and facilitates discussions on sensitive issues. However, only 40% of adolescents have private conversations with clinicians during visits.
View Article and Find Full Text PDFPalliat Med Rep
May 2025
Department of Supportive Care, Division of Palliative Care, University Health Network, Toronto, Canada.
Background: Serious illness communication skills (SICS) are essential competencies for clinicians to possess. Unfortunately, SICS teaching is not routinely taught and many clinician teachers (CTs) never received training on how to teach them. We funded two cohorts of CTs to learn an evidence-based approach to SICS teaching to scale a unified approach to such training.
View Article and Find Full Text PDFFront Pain Res (Lausanne)
August 2025
Center for Veterinary Medicine, Food and Drug Administration, Rockville, MD, United States.
Annually, millions of humans and animals suffer from chronic and acute pain, creating welfare and quality of life concerns for both humans and animals who suffer this pain. In developing new therapeutic approaches, the challenge is to accurately measure this pain to ascertain the efficacy of novel therapeutics. Additionally, there is a need to develop new and effective analgesic options that may offer alternatives to using opioids that contribute to the opioid epidemic.
View Article and Find Full Text PDFRen Fail
December 2025
Department of Nephrology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Large language models (LLMs) represent a transformative advance in artificial intelligence, with growing potential to impact chronic kidney disease (CKD) management. CKD is a complex, highly prevalent condition requiring multifaceted care and substantial patient engagement. Recent developments in LLMs-including conversational AI, multimodal integration, and autonomous agents-offer novel opportunities to enhance patient education, streamline clinical documentation, and support decision-making across nephrology practice.
View Article and Find Full Text PDF