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http://dx.doi.org/10.1002/anie.201509481 | DOI Listing |
Prev Sci
December 2024
Department of Psychiatry and Human Behavior, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
High-quality supervision for teachers in early care and education (ECE) is essential for building positive teacher-child relationships and enhancing ECE program quality, which in turn promotes healthy social-emotional and academic development in young children. Reflective supervision (RS) is a process-oriented and relationship-centered supervisory approach that has growing empirical evidence supporting its use. As the evidence base for RS continues to expand, and early childhood-serving settings-including ECE-increasingly consider this approach, understanding whether RS is likely to be routinely used in ECE settings and what helps or hinders use of this approach is critically important.
View Article and Find Full Text PDFBackground: Burnout is common among residents and negatively impacts patient care and professional development. Residents vary in terms of their experience of burnout. Our objective was to employ cluster analysis, a statistical method of separating participants into discrete groups based on response patterns, to uncover resident burnout profiles using the exhaustion and engagement sub-scales of the Oldenburg Burnout Inventory (OLBI) in a cross-sectional, multispecialty survey of United States medical residents.
View Article and Find Full Text PDFMed Phys
November 2023
Department of Radiation Technology, KKR Sapporo Medical Center, Sapporo, Hokkaido, Japan.
Background: Although flattening filter free (FFF) beams are commonly used in clinical treatment, the accuracy of dose measurements in FFF beams has been questioned. Higher dose per pulse (DPP) such as FFF beams from a linear accelerator may cause problems in dose profile measurements using an ionization chamber due to the change of the charge collection efficiency. Ionization chambers are commonly used for percent depth dose (PDD) measurements.
View Article and Find Full Text PDFBMC Med Res Methodol
June 2023
Department of Psychiatry, University of Pittsburgh, 3811 O'Hara Street, Pittsburgh, PA, 15231, USA.
Background: Machine learning tools such as random forests provide important opportunities for modeling large, complex modern data generated in medicine. Unfortunately, when it comes to understanding why machine learning models are predictive, applied research continues to rely on 'out of bag' (OOB) variable importance metrics (VIMPs) that are known to have considerable shortcomings within the statistics community. After explaining the limitations of OOB VIMPs - including bias towards correlated features and limited interpretability - we describe a modern approach called 'knockoff VIMPs' and explain its advantages.
View Article and Find Full Text PDFNat Commun
June 2023
Bayer AG, Leverkusen, Germany.
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