In this study, we present a newly developed observational system, Optimizing Learning Opportunities for Students (OLOS). OLOS is designed to elucidate the learning opportunities afforded to individual children within early childhood classrooms and as they transition to formal schooling (kindergarten through third grade). OLOS records the time spent in different types of learning opportunities (e.g., play, literacy, math) and the frequency of specific discourse moves children and teachers use (child talk and teacher talk). Importantly, it is being designed to be used validly and reliably by practitioners. Using OLOS, we explored individual children's experiences (n = 68 children in 12 classrooms) in four different types of early childhood programs; state-funded, state-funded PK serving children with disabilities, Head Start, and a tuition-based (non-profit) preschool. Results of our feasibility study revealed that we could feasibly and reliably use OLOS in these very different kinds of pre-kindergarten programs with some changes. OLOS provided data that aligned with our hypotheses and that our practitioner partners found useful. In analysing the observations, we found that individual children's learning opportunities varied significantly both within and between classrooms. In general, we observed that most of the PK day (or half day) was spent in language and literacy activities and non-instructional activities (e.g., transitions). Very little time in math and science was observed yet children were generally more likely to actively participate (i.e., more child talk) during academic learning opportunities (literacy, math, and science). The frequency of teacher talk also varied widely between classrooms and across programs. Plus, the more teacher talk we observed, the more likely we were to observe child talk. Our long-term aim is that OLOS can inform policy and provide information that supports practitioners in meeting the learning and social-behavioral needs of the children they serve.
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http://dx.doi.org/10.1016/j.ecresq.2019.10.001 | DOI Listing |
Nurs Educ Perspect
October 2024
About the Authors Judith Bacchus Cornelius, PhD, RN, FAAN, ANEF, is a professor, College of Health and Human Services, University of North Carolina at Charlotte, Charlotte, North Carolina. Charlene Downing, PhD, RN, is a professor, Department of Nursing, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa. Adesola A. Ogunfowokan, PhD, RN, FWACN, is a professor, Community Health Nursing, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria. Nompumelelo Ntshingila, DCur(UJ), is an associate professor, Department of Nursing, Faculty of Health Sciences, University of Johannesburg. Florence Okoro, PhD, RN, is an associate professor, College of Health and Human Services, University of North Carolina at Charlotte. Ijeoma Enweana, DNP, RN, CVN, is adjunct nursing faculty, Presbyterian School of Nursing, Queens University of Charlotte, Charlotte, North Carolina. Oluwayemisi Olagunju, PhD, is senior lecturer, Department of Nursing Science, Obafemi Awolowo University. Funding was received from the University of North Carolina at Charlotte Global Learning and Internationalization Institute. For more information, contact Dr. Cornelius at
The COVID-19 pandemic presented opportunities for educational innovations and the development of intercultural learning experiences. A global health assignment guided by a collaborative online international learning pedagogy was assigned to doctoral nursing students from three different countries. Icebreaker activities, along with the Culturally You diagram, commenced the team-building process.
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December 2024
National University of Defense Technology, Changsha, Hunan, China.
In-band full-duplex communication has the potential to double the wireless channel capacity. However, how to efficiently transform the full-duplex gain at the physical layer into network throughput improvement is still a challenge, especially in dynamic communication environments. This paper presents a reinforcement learning-based full-duplex (RLFD) medium access control (MAC) protocol for wireless local-area networks (WLANs) with full-duplex access points.
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December 2024
State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach for predicting host health status. Previous research has underscored the potential of microbiome-based predictions, and the rapid advancements of machine learning techniques have introduced new opportunities for exploiting microbiome data. To predict various host nonhealthy conditions, this study proposed an integrated machine learning-based estimation pipeline of Gut Age Index (GAI) by establishing a health aging baseline with the gut microbiome data from healthy individuals.
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December 2024
School of Health & Wellbeing, University of Glasgow, Glasgow, UK.
Introduction: Fear of recurrence is a transdiagnostic problem experienced by people with psychosis, which is associated with anxiety, depression and risk of future relapse events. Despite this, there is a lack of available psychological interventions for fear of recurrence, and psychological therapies for schizophrenia are often poorly implemented in general. However, low-intensity psychological therapy is available for people who experience fear of recurrence in the context of cancer, which means there is an opportunity to learn what has worked in a well-implemented psychological therapy to see if any learning can be adapted for schizophrenia care.
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December 2024
Department of Anaesthesiology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands.
Background: Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases. The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data. Here, we showcase a successful example of a data pipeline to efficiently move patient data to the cloud environment for structured storage.
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