Objective: A practical, reliable, and valid instrument is needed to measure the impact of the learning environment on medical students' well-being and educational experience and to meet medical school accreditation requirements.
Methods: From 2012 to 2015, medical students were surveyed at the end of their first, second, and third year of studies at four medical schools. The survey assessed students' perceptions of the following nine dimensions of the school culture: vitality, self-efficacy, institutional support, relationships/inclusion, values alignment, ethical/moral distress, work-life integration, gender equity, and ethnic minority equity. The internal reliability of each of the nine dimensions was measured. Construct validity was evaluated by assessing relationships predicted by our conceptual model and prior research. Assessment was made of whether the measurements were sensitive to differences over time and across institutions.
Results: Six hundred and eighty-six students completed the survey (49 % women; 9 % underrepresented minorities), with a response rate of 89 % (range over the student cohorts 72-100 %). Internal consistency of each dimension was high (Cronbach's α 0.71-0.86). The instrument was able to detect significant differences in the learning environment across institutions and over time. Construct validity was supported by demonstrating several relationships predicted by our conceptual model.
Conclusions: The C-Change Medical Student Survey is a practical, reliable, and valid instrument for assessing the learning environment of medical students. Because it is sensitive to changes over time and differences across institution, results could potentially be used to facilitate and monitor improvements in the learning environment of medical students.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s40596-016-0620-1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Network-Based Identification of Key Toxic Compounds in Airborne Chemical Exposome.
Environ Sci Technol
January 2025
State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Air pollution is a leading contributor to the global disease burden. However, the complex nature of the chemicals to which humans are exposed through inhalation has obscured the identification of the key compounds responsible for diseases. Here, we develop a network topology-based framework to identify key toxic compounds in the airborne chemical exposome.
View Article and Find Full Text PDF"The Environment Fostered True Growth": Harnessing Coaching on an Innovative Medicine Sub-internship.
J Gen Intern Med
January 2025
Department of Medicine, Division of General Internal Medicine, Section of Hospital Medicine, Weill Cornell Medicine, NewYork-Presbyterian Hospital, New York, NY, USA.
Background: Medicine sub-internships aim to prepare students for residency. However, the traditional sub-internship structure, with multiple learners at varied levels, poses obstacles to providing the clinical exposure, learning environment, and direct observation and feedback necessary to develop essential skills.
Aim: Investigate the educational experience of learners on a coaching-centered sub-internship (CCSI) on a resident uncovered ward service.
Comparison of opportunities and perceptions towards neurosurgery between the public and private sector in Pakistan: a national survey of medical students and recent graduates.
Neurosurg Rev
January 2025
Section of Neurosurgery, Department of Surgery, Aga Khan University Hospital, Karachi, 74800, Pakistan.
Public and private medical institutes must adhere to the same standards of quality set by the Pakistan Medical & Dental Council (PMDC). However, studies have noted varied learning environments. The current study aims to assess opportunities and compare the differences in perceptions between the two sectors.
View Article and Find Full Text PDFUtilizing convolutional neural network (CNN) for orchard irrigation decision-making.
Environ Monit Assess
January 2025
Department of Environmental Management, Graduate School of Agriculture, Kindai University, Nara, Japan.
Efficient agricultural management often relies on farmers' experiential knowledge and demands considerable labor, particularly in regions with challenging terrains. To reduce these burdens, the adoption of smart technologies has garnered increasing attention. This study proposes a convolutional neural network (CNN)-based model as a decision-support tool for smart irrigation in orchard systems, focusing on persimmon cultivation in mountainous regions.
View Article and Find Full Text PDFCu-Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and TEM Study.
Nano Lett
January 2025
Department of Mechanical Engineering & Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States.
The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)45° missing row reconstruction (MRR).
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!