Dataset Information

Structural Competency: Curriculum for Medical Students, Residents, and Interprofessional Teams on the Structural Factors That Produce Health Disparities.

ABSTRACT: Introduction:Research on disparities in health and health care has demonstrated that social, economic, and political factors are key drivers of poor health outcomes. Yet the role of such structural forces on health and health care has been incorporated unevenly into medical training. The framework of structural competency offers a paradigm for training health professionals to recognize and respond to the impact of upstream, structural factors on patient health and health care. Methods:We report on a brief, interprofessional structural competency curriculum implemented in 32 distinct instances between 2015 and 2017 throughout the San Francisco Bay Area. In consultation with medical and interprofessional education experts, we developed open-ended, written-response surveys to qualitatively evaluate this curriculum's impact on participants. Qualitative data from 15 iterations were analyzed via directed thematic analysis, coding language, and concepts to identify key themes. Results:Three core themes emerged from analysis of participants' comments. First, participants valued the curriculum's focus on the application of the structural competency framework in real-world clinical, community, and policy contexts. Second, participants with clinical experience (residents, fellows, and faculty) reported that the curriculum helped them reframe how they thought about patients. Third, participants reported feeling reconnected to their original motivations for entering the health professions. Discussion:This structural competency curriculum fills a gap in health professional education by equipping learners to understand and respond to the role that social, economic, and political structural factors play in patient and community health.

SUBMITTER: Neff J

PROVIDER: S-EPMC7182045 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Structural Competency: Curriculum for Medical Students, Residents, and Interprofessional Teams on the Structural Factors That Produce Health Disparities.

Neff Joshua J Holmes Seth M SM Knight Kelly R KR Strong Shirley S Thompson-Lastad Ariana A McGuinness Cara C Duncan Laura L Saxena Nimish N Harvey Michael J MJ Langford Alice A Carey-Simms Katiana L KL Minahan Sara N SN Satterwhite Shannon S Ruppel Caitlin C Lee Sonia S Walkover Lillian L De Avila Jorge J Lewis Brett B Matthews Jenifer J Nelson Nicholas N

MedEdPORTAL : the journal of teaching and learning resources 20200313

<h4>Introduction</h4>Research on disparities in health and health care has demonstrated that social, economic, and political factors are key drivers of poor health outcomes. Yet the role of such structural forces on health and health care has been incorporated unevenly into medical training. The framework of structural competency offers a paradigm for training health professionals to recognize and respond to the impact of upstream, structural factors on patient health and health care.<h4>Methods ...[more]

PMID: 32342010

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Project description:BackgroundVaccination is the most cost-effective medical intervention known to prevent morbidity and mortality. However, data are limited on the effectiveness of residency programs in delivering immunization knowledge and skills to trainees. The authors sought to describe the immunization competency needs of medical residents at the University of Toronto (UT), and to develop and evaluate a pilot immunization curriculum.MethodsResidents at the University of Toronto across nine specialties were recruited to attend a pilot immunization workshop in November 2018. Participants completed a questionnaire before and after the workshop to assess immunization knowledge and compare baseline change. Feedback was also surveyed on the workshop content and process. Descriptive statistics were performed on the knowledge questionnaire and feedback survey. A paired sample T-test compared questionnaire answers before and after the workshop. Descriptive coding was used to identify themes from the feedback survey.ResultsTwenty residents from at least six residencies completed the pre-workshop knowledge questionnaire, seventeen attended the workshop, and thirteen completed the post-workshop questionnaire. Ninety-five percent (19/20) strongly agreed that vaccine knowledge was important to their career, and they preferred case-based teaching. The proportion of the thirty-four knowledge questions answered correctly increased from 49% before the workshop to 67% afterwards, with a mean of 2.24 (CI: 1.43, 3.04) more correct answers (P < 0.001). Sixteen residents completed the post-workshop feedback survey. Three themes emerged: first, they found the content specific and practical; second, they wanted more case-based learning and for the workshop to be longer; and third, they felt the content and presenters were of high quality.ConclusionsFindings from this study suggest current immunization training of UT residents does not meet their training competency requirements. The study's workshop improved participants' immunization knowledge. The information from this study could be used to develop residency immunization curriculum at UT and beyond.

Project description:BackgroundThe successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process.ObjectiveThe overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians.MethodsWe followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results.ResultsOf the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs.ConclusionsThis review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs.International registered report identifier (irrid)RR2-10.11124/JBIES-22-00374.

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Dataset Information

Structural Competency: Curriculum for Medical Students, Residents, and Interprofessional Teams on the Structural Factors That Produce Health Disparities.

Publications

Structural Competency: Curriculum for Medical Students, Residents, and Interprofessional Teams on the Structural Factors That Produce Health Disparities.

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