While those we learn from are often close to us, more and more our learning environments are shifting to include more distant and dissimilar others. The question we examine in 5 studies is how whom we learn from influences what we learn and how what we learn influences from whom we choose to learn it. In Study 1, we show that social learning, in and of itself, promotes higher level (more abstract) learning than does learning based on one's own direct experience. In Studies 2 and 3, we show that when people learn from and emulate others, they tend to do so at a higher level when learning from a distant model than from a near model. Studies 4 and 5 show that thinking about learning at a higher (compared to a lower) level leads individuals to expand the range of others that they will consider learning from. Study 6 shows that when given an actual choice, people prefer to learn low-level information from near sources and high-level information from distant sources. These results demonstrate a basic link between level of learning and psychological distance in social learning processes.
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http://dx.doi.org/10.1037/pspa0000042 | 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 PDFNat Commun
January 2025
School of Future Technology, University of Chinese Academy of Sciences, 100190, Beijing, PR China.
In bioneuronal systems, the synergistic interaction between mechanosensitive piezo channels and neuronal synapses can convert and transmit pressure signals into complex temporal plastic pulses with excitatory and inhibitory features. However, existing artificial tactile neuromorphic systems struggle to replicate the elaborate temporal plasticity observed between excitatory and inhibitory features in biological systems, which is critical for the biomimetic processing and memorizing of tactile information. Here we demonstrate a mechano-gated iontronic piezomemristor with programmable temporal-tactile plasticity.
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).
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