Introduction: Inclusive morphemes in Spanish, -e y -x, have begun to be used in place of generic masculine forms. In this study, we look at the processing of sentences with inclusive language from the perspective of experimental cognitive psychology and with the methodological tools of psycholinguistics.
Methods: A sentence-by-sentence self-paced reading experiment examined the difference in reading times between sentences containing the traditional, masculine, generic form and sentences with gender inclusive language. The experiment was carried out in 69 monolingual speakers of River Plate Spanish: 38 young adults (between 18 and 30 years: 23 women and 15 men) and 31 older adults (between 31 and 60 years: 12 women and 19 men). Results: sentences with inclusive language were read more slowly than sentences with the generic masculine form. Surprisingly, neither age nor gender were found to have significant effects.
Discussion: These results suggest that reading sentences with inclusive morphemes results in a higher processing cost and that this language change is in its very early stages.
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http://dx.doi.org/10.53680/vertex.v34i159.366 | DOI Listing |
J Am Med Inform Assoc
January 2025
Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN 55455, United States.
Objective: To develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records.
Methods: We focused on 4 core DS information extraction tasks: named entity recognition (2 949 clinical sentences), relation extraction (4 892 sentences), triple extraction (2 949 sentences), and usage classification (2 460 sentences). To address these tasks, we introduced the retrieval-augmented multi-task information extraction (RAMIE) framework, which incorporates: (1) instruction fine-tuning with task-specific prompts; (2) multi-task training of LLMs to enhance storage efficiency and reduce training costs; and (3) retrieval-augmented generation, which retrieves similar examples from the training set to improve task performance.
JMIR Hum Factors
January 2025
Suomen Terveystalo Oy, Suomen Terveystalo Oy, Helsinki, Finland.
Background: Aging brings physical and life changes that could benefit from eHealth services. eHealth holistically combines technology, tasks, individuals, and contexts, and all these intertwined elements should be considered in eHealth development. As users' needs change with life situations, including aging and retirement, it is important to identify these needs at different life stages to develop eHealth services for well-being and active, healthy lives.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 21955, Saudi Arabia.
Sign language (SL) is a means of communication that is used to bridge the gap between the deaf, hearing-impaired, and others. For Arabic speakers who are hard of hearing or deaf, Arabic Sign Language (ArSL) is a form of nonverbal communication. The development of effective Arabic sign language recognition (ArSLR) tools helps facilitate this communication, especially for people who are not familiar with ArSLR.
View Article and Find Full Text PDFMed Decis Making
December 2024
Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, the Netherlands.
Objective: Patient decision aids (PtDAs) can support shared decision making. We aimed to explore how inclusive PtDAs are for people with limited health literacy (LHL) by analyzing 1) the understandability of PtDAs using established criteria, 2) how options and probabilities of outcomes are communicated, and 3) the extent to which risk communication (RC) guidelines are followed.
Methods: In a descriptive document analysis, we analyzed Dutch PtDAs available in 2021 that met the International Patient Decision Aid Standards.
J Med Internet Res
December 2024
Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Background: Systematic reviews (SRs) are considered the highest level of evidence, but their rigorous literature screening process can be time-consuming and resource-intensive. This is particularly challenging given the rapid pace of medical advancements, which can quickly make SRs outdated. Few-shot learning (FSL), a machine learning approach that learns effectively from limited data, offers a potential solution to streamline this process.
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