Primarily grounded in Zimmerman's social cognitive model of self-regulation, graduate medical education is guided by principles that self-regulated learning takes place within social context and influence, and that the social context and physical environment reciprocally influence persons and their cognition, behavior, and development. However, contemporary perspectives on self-regulation are moving beyond Zimmerman's triadic reciprocal orientation to models that consider social transactions as the central core of regulated learning. Such co-regulated learning models emphasize shared control of learning and the role more advanced others play in scaffolding novices' metacognitive engagement.Models of co-regulated learning describe social transactions as periods of distributed regulation among individuals, which instrumentally promote or inhibit the capacity for individuals to independently self-regulate. Social transactions with other regulators, including attending physicians, more experienced residents, and allied health care professionals, are known to mediate residents' learning and to support or hamper the development of their self-regulated learning competence. Given that social transactions are at the heart of learning-oriented assessment and entrustment decisions, an appreciation for co-regulated learning is likely important for advancing medical education research and practice-especially given the momentum of new innovations such as entrustable professional activities.In this article, the author explains why graduate medical educators should consider adopting a model of co-regulated learning to complement and extend Zimmerman's models of self-regulated learning. In doing so, the author suggests a model of co-regulated learning and provides practical examples of how the model is relevant to graduate medical education research and practice.
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http://dx.doi.org/10.1097/ACM.0000000000001583 | DOI Listing |
Med Educ
January 2025
Institute of Health Professions Education Assessment and Reform, China Medical University, Shenyang, China.
Front Biosci (Landmark Ed)
November 2024
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, 230022 Hefei, Anhui, China.
Background: Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 () in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, 's comprehensive impact on aneuploidy incidence across different cancer types remains unexplored.
View Article and Find Full Text PDFAm J Hum Genet
December 2024
Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institution, Tampa, FL 33612, USA. Electronic address:
Med Educ
October 2024
Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Background/objective: In implementing competence-based medical education (CBME), some Canadian residency programmes recruit clinicians to function as Academic Advisors (AAs). AAs are expected to help monitor residents' progress, coach them longitudinally, and serve as sources of co-regulated learning (Co-RL) to support their developing self-regulated learning (SRL) abilities. Implementing the AA role is optional, meaning each residency programme must decide whether and how to implement it, which could generate uncertainty and heterogeneity in how effectively AAs will "monitor and advise" residents.
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November 2024
Department of Civil and Environmental Engineering, University of Macau, Macau SAR, China. Electronic address:
Extensive studies on improving anammox performance have taken place for decades with particular focuses on its operational and environmental factors, but such parameter-based optimization is difficult, because of the sheer number of possible combinations and multidimensional arrays of these factors. Utilizing machine-learning algorithm and published anammox data, Bayesian nonparametric general regression (BNGR) was applied to identify the possible governing variable(s) from among 11 operating and environmental parameters: reactor type, mixing type, working volume, hydraulic retention time, temperature, influent pH, nitrite, ammonium, nitrate concentration, nitrogen loading rate, and organic concentration. The results showed that working volume is a key but oft-overlooked governing parameter.
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