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      Importance of multimodal resident education curriculum for general surgeons: perspectives of trainers and trainees

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          Abstract

          Purpose

          Satisfaction should be prioritized to maximize the value of education for trainees. This study was conducted with professors, fellows, and surgical residents in the Department of general surgery (GS) to evaluate the importance of various educational modules to surgical residents.

          Methods

          A questionnaire was administered to professors ( n = 28), fellows ( n = 8), and surgical residents ( n = 14), and the responses of the three groups were compared. Four different categories of educational curricula were considered: instructor-led training, clinical education, self-paced learning, and hands-on training.

          Results

          The majority of surgeons regarded attending scrubs as the most important educational module in the training of surgical residents. However, while professors identified assisting operators by participating in surgery as the most important, residents assessed the laparoscopic training module with animal models as the most beneficial.

          Conclusions

          The best educational training course for surgical residents was hands-on training, which would provide them with several opportunities to operate and perform surgical procedures themselves.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12909-024-05515-x.

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          Most cited references26

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          Representation learning: a review and new perspectives.

          Y Bengio, A. Courville, P. Vincent (2013)
          The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
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            Surgeon fatigue: a prospective analysis of the incidence, risk, and intervals of predicted fatigue-related impairment in residents.

            Frank McCormick, John Kadzielski, Christopher Landrigan … (2012)
            A novel approach to identify at-risk periods among orthopedic surgical residents may direct fatigue risk mitigation and facilitate targeted interventions. A prospective cohort study with a minimum 2-week continuous assessment period. Data on sleep and awake periods were processed using the sleep, activity, fatigue, and task effectiveness model. Rotations at 2 academic tertiary care centers. Twenty-seven of 33 volunteer orthopedic surgical residents (82%) completed the study, representing 65% (33 of 51) of the orthopedic residency program. Residents' sleep and awake periods were continuously recorded via actigraphy, and a daily questionnaire was used to analyze mental fatigue. Percentage of time at less than 80% mental effectiveness (correlating with an increased risk of error), percentage of time at less than 70% mental effectiveness (correlating with a blood alcohol level of 0.08%), the mean amount of daily sleep, and the relative risk of medical error compared with chance. Residents were fatigued during 48% and impaired during 27% of their time awake. Among all residents, the mean amount of daily sleep was 5.3 hours. Overall, residents' fatigue levels were predicted to increase the risk of medical error by 22% compared with well-rested historical control subjects. Night-float residents were more impaired (P = .02), with an increased risk of medical error (P = .045). Resident fatigue is prevalent, pervasive, and variable. To guide targeted interventions, fatigue modeling can be conducted in hospitals to identify periods, rotations, and individuals at risk of medical error.
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              From Modules to MOOCs : Application of the Six-Step Approach to Online Curriculum Development for Medical Education

              Belinda Y. Chen, David E. Kern, Robert M. Kearns … (2019)
              Article 0 comments Cited 43 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Jin Hyang Jung: jjh01@knu.ac.kr
                Journal
                Journal ID (nlm-ta): BMC Med Educ
                Journal ID (iso-abbrev): BMC Med Educ
                Title: BMC Medical Education
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1472-6920
                Publication date (Electronic): 10 May 2024
                Publication date PMC-release: 10 May 2024
                Publication date Collection: 2024
                Volume: 24
                Electronic Location Identifier: 518
                Affiliations
                Department of Surgery, Kyungpook National University Chilgok Hospital, ( https://ror.org/040c17130) Hoguk-Ro 807, Buk-Gu, 41404 Daegu, Republic of Korea
                Article
                Publisher ID: 5515
                DOI: 10.1186/s12909-024-05515-x
                PMC ID: 11088119
                PubMed ID: 38730375
                SO-VID: 811e9b50-c90a-4423-be17-9699b13259fc
                Copyright © © The Author(s) 2024
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 8 October 2023
                Date accepted : 3 May 2024
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © BioMed Central Ltd., part of Springer Nature 2024

                ScienceOpen disciplines: Education
                Keywords: surgeon,residency,education,multimodal,curriculum
                Data availability:
                ScienceOpen disciplines: Education
                Keywords: surgeon, residency, education, multimodal, curriculum

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