Unstructured categories are those in which the stimuli are assigned to each contrasting category randomly, and thus there is no rule- or similarity-based strategy for determining category membership. Intuition suggests that unstructured categories are likely to be learned via explicit memorization that is under the control of declarative memory. In contrast to this prediction, neuroimaging studies of unstructured-category learning have reported task-related activation in the striatum, but typically not in the hippocampus--results that seem more consistent with procedural learning than with a declarative-memory strategy. This article reports the first known behavioral test of whether unstructured-category learning is mediated by explicit strategies or by procedural learning. Our results suggest that the feedback-based learning of unstructured categories is mediated by procedural memory.
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http://dx.doi.org/10.3758/s13423-012-0312-0 | DOI Listing |
JMIR Med Inform
January 2025
School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States.
Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.
Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).
BMC Health Serv Res
January 2025
Care Directorate, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil 4, Geneva, 1205, Switzerland.
Background: The evolving healthcare landscape emphasizes the need for health systems to adapt to growing complexities, with new models of care enabling healthcare providers to optimize their scope of practice and coordination of care. Despite increasing interest in advanced practice, confusion persists regarding the roles and scopes of practice of healthcare providers, exacerbated by variations in regulations and titles. We sought to clarify the differences between specialized healthcare professionals, practitioners, and clinical specialists; to describe their roles; and to propose initiatives aimed at supporting the implementation of advanced practice within a university hospital.
View Article and Find Full Text PDFJ Paediatr Child Health
January 2025
School of Medicine and Psychology, College of Health and Medicine, Australian National University, Acton, Australia.
Background: Hospital care for neonates can be challenging for parents, and a negative parental experience can affect the well-being of the infant after discharge. A family-centred approach is the gold standard of care in neonatology.
Aim: This study aimed to identify common themes in voluntary unstructured feedback received from parents and caregivers of infants admitted to the neonatal intensive care unit, special care nursery or postnatal ward or followed up by neonatal outpatient services at a tertiary Australian Women and Children's Hospital.
Appl Clin Inform
January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
Objective: Commercially available large language models such as Chat Generative Pre-Trained Transformer (ChatGPT) cannot be applied to real patient data for data protection reasons. At the same time, de-identification of clinical unstructured data is a tedious and time-consuming task when done manually. Since transformer models can efficiently process and analyze large amounts of text data, our study aims to explore the impact of a large training dataset on the performance of this task.
View Article and Find Full Text PDFFront Genet
December 2024
Department of Economics, Faculty of Social Sciences, Brock University, St. Catharines, ON, Canada.
Although lab-coat genomics scientists are highly skilled and involved in pioneering work, few studies have examined their perceptions on what they do, and how they relate with others in interdisciplinary work. Recognizing that gap, we were curious to talk with scientists about their current work and positionalities related to the use of genomics for bioremediation. Using unstructured open-ended interviews and thematic analysis, we interviewed researchers with diverse genomics-related expertise.
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