Purpose: Medical students' transition to postgraduate training, given the complexity of new roles and responsibilities, requires the engagement of all involved stakeholders. This study aims to co-create a transition curriculum and determine the value of involving the key stakeholders throughout such transition in its design process.
Methods: We conducted a mixed-methods study involving faculty/leaders (undergraduate/postgraduate), final-year medical students, and chief residents. It commenced with eight co-creation sessions (CCS), qualitative results of which were used to draft a quantitative survey sent to non-participants, followed by two consensus-building CCS with the original participants. We applied thematic analysis for transcripts of all CCS, and mean scores with standard deviations for survey analysis.
Results: We identified five themes: adaptation, authenticity, autonomy, connectedness, and continuity, embedded in the foundation of a supportive environment, to constitute a Model of Learning during Transition (MOLT). Inclusion of various stakeholders and optimizing their representation brought rich perspectives to the design process. This was reinforced through active students' participation enabling a final consensus.
Conclusions: Bringing perspectives of key stakeholders in the transition spectrum enriches transition curricula. The proposed MOLT can provide a guide for curriculum designers to optimize the final year of undergraduate medical training in preparing students for postgraduate training with essential competencies to be trained.
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http://dx.doi.org/10.1080/0142159X.2022.2118037 | DOI Listing |
J Mol Model
January 2025
Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
View Article and Find Full Text PDFBiomech Model Mechanobiol
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.
View Article and Find Full Text PDFBrain Struct Funct
January 2025
Department of Biomedical Engineering, College of Chemistry and Life Sciences, Beijing University of Technology, Beijing, 100124, China.
The brain undergoes atrophy and cognitive decline with advancing age. The utilization of brain age prediction represents a pioneering methodology in the examination of brain aging. This study aims to develop a deep learning model with high predictive accuracy and interpretability for brain age prediction tasks.
View Article and Find Full Text PDFJ Youth Adolesc
January 2025
Research Center of Adolescent Psychology and Behavior, School of Education, Guangzhou University, Guangzhou, China.
Risk-taking is a concerning yet prevalent issue during adolescence and can be life-threatening. Examining its etiological sources and evolving pathways helps inform strategies to mitigate adolescents' risk-taking behavior. Studies have found that unfavorable environmental factors, such as adverse childhood experiences (ACEs), are associated with momentary levels of risk-taking in adolescents, but little is known about whether ACEs shape the developmental trajectory of risk-taking.
View Article and Find Full Text PDFPediatr Cardiol
January 2025
Department of Infectious Disease, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, No. 1678 Dongfang Road, Pudong New Area, Shanghai, 200127, China.
Kawasaki disease (KD) is a febrile vasculitis disorder, with coronary artery lesions (CALs) being the most severe complication. Early detection of CALs is challenging due to limitations in echocardiographic equipment (UCG). This study aimed to develop and validate an artificial intelligence algorithm to distinguish CALs in KD patients and support diagnostic decision-making at admission.
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