It is commonly claimed that higher domain knowledge enhances new learning-the knowledge-is-power hypothesis. However, a recent meta-analysis (Simonsmeier et al., 2022) has challenged this idea, finding no overall relationship between prior knowledge and new learning across hundreds of highly variable effect sizes. The authors note that this variability and lack of randomized controlled experiments preclude broad claims regarding the influence of prior knowledge on learning. The present study (conducted in 2020) provides an experimental assessment of the causal effect of prior domain knowledge on new learning. Participants were randomly assigned to receive training in one of two academic domains over 3 days before learning new information about topics in both domains for a later test. Training was specific to three of four topics within that domain, allowing the untrained topic in the trained domain to act as a measure of new learning in that domain. New learning, measured as final test performance or knowledge gains, did not differ between the high and low domain knowledge conditions. Experimentally induced prior domain knowledge did not affect new learning. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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BMC Med Educ
January 2025
School of Allied Health Science and Practice, Engineering Math and Science Building, University of Adelaide, North Terrace, Level 4, Adelaide, South Australia, 5005, Australia.
Background: Training programs grounded in educational theory offer a systematic framework to facilitate learning and outcomes. This scoping review aims to map the educational approaches documented for manual wheelchair training and to record intended learning outcomes and any relationships between learning theories, instructional design and outcomes.
Methods: Eight databases; Cochrane's Library, EMBASE, CINAHL, PubMed, Scopus, EmCare, Medline, ProQuest Nursing and Allied Health Database and grey literature were searched in September 2023, with citation chaining for relevant papers.
BMC Public Health
January 2025
School of Health Management Policy, Chinese Academy of Medical Sciences & Peking Union Medical College, No. 46 Xizongbu Hutong, Dongcheng District, Beijing, 100730, People's Republic of China.
Background: Physical literacy (PL) is pivotal for improving sedentary behaviors, enhancing intrinsic motivation for physical activity, and supporting the growth and development of adolescents. This study aims to measure the current situation and internal pathway of PL among Chinese elementary school students.
Methods: This study was conducted from June to July 2022 and used multistage cluster sampling to select the study subjects.
Environ Int
January 2025
Einstein Excellence Office, Hospital Israelita Albert Einstein - Paulista Av, 2300 - Consolação. Zip code: 01310-300 - São Paulo, Brazil. Electronic address:
Background: Nature-based interventions (NBIs) benefit human well-being, but clinical trials comparing different NBIs in various natural environments are scarce.
Objective: To evaluate the efficacy of a multicomponent nature-based intervention (MNBI) in comparison to control group (classical forest bathing).
Primary Outcome: well-being; Secondary outcomes: vitality, happiness, connection, and engagement with nature across urban, peri-urban, and rural areas.
Nurse Educ Today
January 2025
Lecturer in Nursing Education, Faculty of Nursing, Midwifery & Palliative Care, King's College London, 57 Waterloo Road, London, SE1 8WA. Electronic address:
Background/problems: Individuals with comorbid physical and mental health conditions face significant threats to their well-being while placing a substantial burden on healthcare systems through increased service costs. Nursing professionals encounter multiple challenges in delivering effective care to this population. These challenges include a lack of integrated care models, communication barriers among providers, the complexity of addressing dual health needs, insufficient training in comorbidity management, resource and time constraints, and pervasive stigma toward mental illness.
View Article and Find Full Text PDFPhysiol Meas
January 2025
Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10 postbus 2440 3001 LEUVEN Belgium, Leuven, Flanders, 3000, BELGIUM.
Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx, a Python library designed to support the analysis of sleep stages using deep learning and Explainable AI (XAI). Approach: PhysioEx provides an extensible and modular API for standardizing and automating the sleep staging pipeline, covering data preprocessing, model training, testing, fine-tuning, and explainability.
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