Background: Professionalism is an important part of the hidden curriculum that is gaining attention in surgical education. McMaster University, Hamilton, Ontario, Canada, has introduced a small group discussion model using critical incident reports (CIRs) to elicit students' reflections on ethical, communication, and professionalism challenges during surgical clerkship. We described the themes identified by surgical clerks in their CIRs.
Methods: Using thematic analysis, 4 investigators coded 64 CIRs iteratively until conceptual saturation. Rigor and validity were ensured throughout the process. Data were further explored to compare the CIRs of junior and senior clerks.
Results: Twenty-seven themes and 4 relationship domains emerged: the clerk's relationship to patients, the health care team, the health care system, and self. Challenges with communication, the consent process, and breaking bad news were most commonly cited. Theme frequencies differed between junior and senior clerks.
Conclusions: Small group discussions of critical incident reports allow surgical clerks to reflect on their developing professional relationships. The themes that have been identified can be used to guide professionalism education and uncover the hidden curriculum.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.amjsurg.2012.12.001 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Nurse lecturers' experiences of working with people with lived experience: A phenomenological study.
Nurse Educ Today
December 2024
Anglia Ruskin University, Cambridge, UK. Electronic address:
Background: Involving people with lived experience in United Kingdom healthcare courses is a government directive and professional body recommendation, yet involvement remains non-standardised with minimal guidance. Previous literature has largely ignored the experiences of Nurse lecturer's in this work, yet they provide vital resources in promoting, sustaining and developing the involvement of people with lived experience.
Aim: To explore adult nurse lecturers' experiences of working with people with lived experience in two higher educational institution settings.
Machine Learning Algorithm-Based Prediction of Diabetes Among Female Population Using PIMA Dataset.
Healthcare (Basel)
December 2024
Department of Computer Science, School of Arts, Humanities and Social Sciences, University of Roehampton, London SW15 5PH, UK.
: Diabetes is a metabolic disorder characterized by increased blood sugar levels. Early detection of diabetes could help individuals to manage and delay the progression of this disorder effectively. Machine learning (ML) methods are important in forecasting the progression and diagnosis of different medical problems with better accuracy.
View Article and Find Full Text PDFThe Impact of Organisational Interventions on Student Perceptions of the Learning Environment: A Repeated Cross-Sectional Study.
Clin Teach
February 2025
Department of Internal Medicine, University of California Davis School of Medicine, Sacramento, California, USA.
Background: The learning environment (LE) refers to the social interactions, organisational culture and physical spaces that shape learners' perceptions and learning. With numerous efforts to measure and improve it, there is still a lack of clearly identified, evidence-based interventions that impact the LE. Our aims were to design LE interventions and measure their effectiveness using a comparison of student responses on the Association of American Medical Colleges Graduation Questionnaire (AAMC GQ).
View Article and Find Full Text PDFMulti-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.
PLoS Comput Biol
January 2025
Complex Systems Research Center, Shanxi University, Taiyuan, Shanxi, China.
Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction.
View Article and Find Full Text PDFDeveloping clinical reasoning along the cognitive continuum: a mixed methods evaluation of a novel Clinical Diagnosis Assessment.
BMC Med Educ
January 2025
Deakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
Background: Clinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties.
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!