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Background: Pediatricians require primary palliative care (PC) skills - communication, pain and symptom management, and psychosocial support - to provide care that mitigates suffering for children with serious illnesses. Residents may not develop skills adequately, and little is known about how they learn those that they do have.
Objective: To explore effective primary PC learning in Canadian pediatrics residency programs.
Methods: Using Appreciative Inquiry methodology, we focused on 'what is working well' to explore resident learning. We purposively sampled 17 trainees (post-graduate years 3-5), representing 13/17 programs. Participants engaged in semi-structured interviews, which we transcribed and analyzed iteratively through an inductive thematic process.
Results: The findings highlighted two predominant themes: a) Embracing incidental learning in the workplace, and b) Scaffolding learning through balanced structure and autonomy. Subthemes included: Recognizing the value of informal and unexpected learning opportunities; Strategies for harnessing incidental learning; Fostering interprofessional collaboration for learning; Integrating PC throughout training; Balancing structured learning with workplace-based opportunities for skill development; and the importance of graduated responsibility in workplace learning.
Conclusions: The residency learning environment provides a rich milieu to develop primary PC skills, but it is often difficult to make use of the fragmented learning opportunities. Residents rely significantly on unplanned clinical opportunities and must actively engage in planning, monitoring, and reflecting on their experiences to develop these skills. Our study underscores the importance of a multi-faceted approach to acquisition of PC skills - through experiential learning, reflective practice, graded responsibility, mentorship opportunities - spread throughout the duration of training.
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http://dx.doi.org/10.1016/j.acap.2025.103131 | DOI Listing |
J Clin Invest
September 2025
The University of Texas at Austin, Austin, United States of America.
Background: Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which debilitating symptoms persist for at least three months. Elucidating biologic underpinnings of LC could identify therapeutic opportunities.
Methods: We utilized machine learning methods on biologic analytes provided over 12-months after hospital discharge from >500 COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor", trained on patient-reported physical function survey scores.
Cereb Cortex
August 2025
Department of Developmental Psychology, University of Amsterdam, Nieuwe Achtergracht 129b, 1018 WS Amsterdam, The Netherlands.
Social learning, a hallmark of human behavior, entails integrating other's actions or ideas with one's own. While it can accelerate the learning process by circumventing slow and costly individual trial-and-error learning, its effectiveness depends on knowing when and whose information to use. In this study, we explored how individuals use social information based on their own and others' levels of uncertainty.
View Article and Find Full Text PDFCereb Cortex
August 2025
Faculty of Psychology and Education Science, Department of Psychology, University of Geneva, Chemin des Mines 9, Geneva, 1202, Switzerland.
Language learning and use relies on domain-specific, domain-general cognitive and sensory-motor functions. Using fMRI during story listening and behavioral tests, we investigated brain-behavior associations between linguistic and non-linguistic measures in individuals with varied multilingual experience and reading skills, including typical reading participants (TRs) and dyslexic readers (DRs). Partial Least Square Correlation revealed a main component linking cognitive, linguistic, and phonological measures to amodal/associative brain areas.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
September 2025
Max Planck Institute for Solar System Research, Göttingen 37077, Germany.
Turbulent convection governs heat transport in both natural and industrial settings, yet optimizing it under extreme conditions remains a significant challenge. Traditional control strategies, such as predefined temperature modulation, struggle to achieve substantial enhancement. Here, we introduce a deep reinforcement learning (DRL) framework that autonomously discovers optimal control policies to maximize heat transfer in turbulent Rayleigh-Bénard convection.
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