Random call is a randomized approach to select a student or group of students to share their thinking with the whole class. There are potential costs and benefits of random call in undergraduate courses, yet we lack insight about how this strategy is actually implemented and why instructors choose to use it. We interviewed 12 college biology instructors who use random call in courses with 50 or more students. Qualitative content analysis revealed why these instructors chose to use random call, the specific ways they implemented random call, and the reasoning behind their implementation. Instructors used random call to increase the diversity of voices heard in the classroom and to hold students accountable for working. Random call users showed concern about student anxiety and took specific steps to mitigate it. We break random call down into a series of components, identify the components that our participants considered most critical, and describe the reasoning underlying random call components. This work lays a foundation for future investigations of how specific random call components influence student outcomes, in what contexts, and for which students.
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http://dx.doi.org/10.1187/cbe.19-07-0130 | DOI Listing |
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFInt Nurs Rev
March 2025
College of Nursing, ShaoYang University, Shaoyang, China.
Background: While numerous studies have quantified the prevalence of reasons for missed care, a comprehensive synthesis of evidence across various health systems remains lacking.
Aim: To estimate the pooled prevalence of the reasons reported by nurses for missed care, using data from the MISSCARE surveys.
Introduction: Missed nursing care, which refers to any aspect of essential patient care that is omitted or delayed, presents substantial risks to patient safety and the quality of care.
Prehosp Emerg Care
January 2025
Department of Emergency Medicine, MetroHealth Medical Center, Cleveland, OH.
Objectives: Opioid-associated fatal and non-fatal overdose rates continue to rise. Prehospital overdose education and naloxone distribution (OEND) programs are attractive harm-reduction strategies, as patients who are not transported by EMS after receiving naloxone have limited access to other interventions. This narrative summary describes our experiences with prehospital implementation of evidence-based OEND practices across Ohio as part of the HEALing Communities Study (HCS).
View Article and Find Full Text PDFEnviron Sci Process Impacts
January 2025
Department of Civil, Architectural, and Environmental Engineering, Illinois Institute of Technology, Alumni Memorial Hall Room 228, 3201 South Dearborn Street, Chicago, IL 60616, USA.
There is an increasing number of randomized clinical trials intended to assess the effectiveness of indoor air cleaners for improving participant outcomes in real-world settings. In this communication, we synthesize the current state of registered air cleaner intervention trials and call attention to the critical importance of conducting measurements to characterize the performance and utilization of air cleaners in such trials to improve interpretation of exposure measurements and patient outcomes. We draw upon the existing literature and preliminary findings from our ongoing one-year, randomized, single-blind, placebo-controlled case-control trial of stand-alone air filtration in the homes of U.
View Article and Find Full Text PDFInt J Med Inform
January 2025
IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
Background: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the characteristics of patients who are most likely to drop out, and to evaluate the potential cost effectiveness of the implementation of a follow-up system based on the obtained data.
Methods: All patients recruited in the local spine surgery registry were included and demographic, peri- and postoperative data were collected.
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