Social learning is considered one of the hallmarks of cognition. Observers learn from demonstrators that a particular behavior pattern leads to a specific consequence or outcome, which may be either positive or negative. In the last few years, social learning has been studied in a variety of taxa including birds and bony fish. To date, there are few studies demonstrating learning processes in cartilaginous fish. Our study shows that the cartilaginous fish freshwater stingrays (Potamotrygon falkneri) are capable of social learning and isolates the processes involved. Using a task that required animals to learn to remove a food reward from a tube, we found that observers needed significantly (P < 0.01) fewer trials to learn to extract the reward than demonstrators. Furthermore, observers immediately showed a significantly (P < 0.05) higher frequency of the most efficient "suck and undulation" strategy exhibited by the experienced demonstrators, suggesting imitation. Shedding light on social learning processes in cartilaginous fish advances the systematic comparison of cognition between aquatic and terrestrial vertebrates and helps unravel the evolutionary origins of social cognition.
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http://dx.doi.org/10.1007/s10071-013-0625-z | DOI Listing |
J Adv Nurs
January 2025
Nursing Practice Development Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.
Aims: To evaluate the implementation process of a novel program focused on improving interactive (dialogic) feedback between clinicians and students during placement.
Design: Quantitative cross-sectional hybrid type 3 effectiveness-implementation study driven by a federated model of social learning theory and implementation theory.
Methods: From June to November 2018, feedback approaches supported by socio-constructive learning theory and Normalisation Process Theory were enacted in four clinical units of a healthcare facility in southeast Queensland, Australia.
Aim: To discuss inter-organisational collaboration in the context of the successful COVID-19 vaccination programme in North Central London (NCL).
Design: An action research study in 2023-2024.
Methods: Six action research cycles used mixed qualitative methods.
AIDS Behav
January 2025
Institute for Sexual and Gender Minority Health and Wellbeing, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Young men who have sex with men (YMSM) have high rates of substance use, which increases their risk for HIV. Digital Health Interventions (DHI) have the potential to address HIV risk overall and reduce harms in the context of substance use. However, there is limited research on how YMSM with different substance use patterns respond to HIV DHIs and how these programs impact participant outcomes.
View Article and Find Full Text PDFJMIR 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).
Environ Res
January 2025
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel.
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a daily high-resolution PM machine-learning model covering the contiguous US from 2003 through 2023. XIS uses aerosol optical depth from satellites and a parsimonious set of additional predictors to make predictions at arbitrary points, capturing near-roadway gradients and allowing the estimation of address-level exposures.
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