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Using generic language to describe groups (applying characteristics to entire categories) is ubiquitous and affects how children and adults categorize other people. Five-year-olds, 8-year-olds, and adults ( N = 190) learned about a novel social group that separated into two factions (citizens and noncitizens). Noncitizens were described in either generic or specific language. Later, the children and adults categorized individuals in two contexts: criminal (individuals labeled as noncitizens faced jail and deportation) and noncriminal (labeling had no consequences). Language genericity influenced decision making. Participants in the specific-language condition, but not those in the generic-language condition, reduced the rate at which they identified potential noncitizens when their judgments resulted in criminal penalties compared with when their judgments had no consequences. In addition, learning about noncitizens in specific language (vs. generic language) increased the amount of matching evidence participants needed to identify potential noncitizens (preponderance standard) and decreased participants' certainty in their judgments. Thus, generic language encourages children and adults to categorize individuals using a lower evidentiary standard regardless of negative consequences for presumed social-group membership.
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http://dx.doi.org/10.1177/0956797617714827 | DOI Listing |
Ann Palliat Med
September 2025
Department of Palliative Care, Rehabilitation and Integrative Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Background: Delirium is a common condition at the end of life and causes significant distress in patients and their loved ones. A precipitant factor can be found in less than half of the patients and the management interventions are limited.
Case Description: A patient in his late sixties with low English proficiency with a metastatic neuroendocrine tumor was transferred to a palliative care unit on non-invasive bilevel ventilation.
J Midwifery Womens Health
September 2025
General Education Department Chair, Midwives College of Utah, Salt Lake City, Utah.
Applications driven by large language models (LLMs) are reshaping higher education by offering innovative tools that enhance learning, streamline administrative tasks, and support scholarly work. However, their integration into education institutions raises ethical concerns related to bias, misinformation, and academic integrity, necessitating thoughtful institutional responses. This article explores the evolving role of LLMs in midwifery higher education, providing historical context, key capabilities, and ethical considerations.
View Article and Find Full Text PDFObjectives: The primary aim of this study was to compare resource utilization between lower and higher-risk brief resolved unexplained events (BRUE) in the general (GED) and pediatric (PED) emergency departments.
Methods: We conducted a retrospective chart review of BRUE cases from a large health system over 6-and-a-half years. Our primary outcome was the count of diagnostic tests per encounter.
Behav Res Methods
September 2025
Laboratoire de Psychologie, Université de Bordeaux, LabPsy UR 4139, 3 Place de la Victoire, 33076, Bordeaux Cedex, France.
This article presents a new set of semantic feature production norms, collected from 580 young adults, for 360 French concepts across various semantic categories. Although empirically derived feature norms have been developed for several languages and have been shown to be useful for investigating semantic memory and providing assessment tools, none are currently available for native French-speaking populations. In this study, the participants performed a property generation task in which they were asked to list features to describe the characteristics of each given concept (e.
View Article and Find Full Text PDFJ Imaging Inform Med
September 2025
Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital and University of Bern, Bern, Switzerland.
Large language models (LLMs) have been successfully used for data extraction from free-text radiology reports. Most current studies were conducted with LLMs accessed via an application programming interface (API). We evaluated the feasibility of using open-source LLMs, deployed on limited local hardware resources for data extraction from free-text mammography reports, using a common data element (CDE)-based structure.
View Article and Find Full Text PDF