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Shale L. Wong

Bio: Shale L. Wong is an academic researcher from University of Colorado Denver. The author has contributed to research in topics: Health policy & Health care. The author has an hindex of 5, co-authored 22 publications receiving 291 citations.

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TL;DR: The authors propose a definition and, using the biographies of actual physician advocates, describe the spectrum of physician advocacy, as first steps toward building a model for competency-based physician advocacy training and delineating physician advocacy in common practice.
Abstract: Many medical authors and organizations have called for physician advocacy as a core component of medical professionalism. Despite widespread acceptance of advocacy as a professional obligation, the concept remains problematic within the profession of medicine because it remains undefined in concept, scope, and practice. If advocacy is to be a professional imperative, then medical schools and graduate education programs must deliberately train physicians as advocates. Accrediting bodies must clearly define advocacy competencies, and all physicians must meet them at some basic level. Sustaining and fostering physician advocacy will require modest changes to both undergraduate and graduate medical education. Developing advocacy training and practice opportunities for practicing physicians will also be necessary. In this article, as first steps toward building a model for competency-based physician advocacy training and delineating physician advocacy in common practice, the authors propose a definition and, using the biographies of actual physician advocates, describe the spectrum of physician advocacy.

223 citations

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TL;DR: A curriculum in advocacy and leadership skills that includes an intensive, community-based service learning experience is effective at increasing student empowerment and disposition toward community service.
Abstract: Introduction Traditional medical training focuses on ameliorating disease states but not on the underlying socially determined causes. The LEADS (Leadership Education Advocacy Development Scholarship) program at the University of Colorado Denver School of Medicine was designed to train medical students to become effective advocates and to promote health at the community level. Methods Participants in the LEADS Track complete courses in advocacy skills, perform a summer internship, and complete a mentored scholarly activity addressing population health. Students are paired with a faculty mentor and a community-based organization. Results Students report empowerment, improved self-efficacy, and increased likelihood of future engagement in leadership and health advocacy. Community sponsors also rate the experience as highly valuable. Conclusions A curriculum in advocacy and leadership skills that includes an intensive, community-based service learning experience is effective at increasing student empowerment and disposition toward community service.

62 citations

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TL;DR: This research presents a novel and scalable approach called “informed consent” that allows for real-time decision-making about whether or not to vaccinate children against infectious disease.

40 citations

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TL;DR: A broad yet comprehensive set of domains to categorize advocacy activities, including advocacy engagement, knowledge dissemination, community outreach, advocacy teaching/mentoring, and advocacy leadership/administration are proposed.
Abstract: Recent changes in health care delivery systems and in medical training have primed academia for a paradigm shift, with strengthened support for an expanded definition of scholarship. Physicians who consider advocacy to be relevant to their scholarly endeavors need a standardized format to display activities and measure the value of health outcomes to which their work can be attributed. Similar to the Educator Portfolio, the authors here propose the Advocacy Portfolio (AP) to document a scholarly approach to advocacy.Despite common challenges faced in the arguments for both education and advocacy to be viewed as scholarship, the authors highlight inherent differences between the two fields. On the basis of prior literature, the authors propose a broad yet comprehensive set of domains to categorize advocacy activities, including advocacy engagement, knowledge dissemination, community outreach, advocacy teaching/mentoring, and advocacy leadership/administration. Documenting quality, quantity, and a scholarly approach to advocacy within each domain is the first of many steps to establish congruence between advocacy and scholarship for physicians using the AP format.This standardized format can be applied in a variety of settings, from medical training to academic promotion. Such documentation will encourage institutional buy-in by aligning measured outcomes with institutional missions. The AP will also provide physician-advocates with a method to display the impact of advocacy projects on health outcomes for patients and populations. Future challenges to broad application include establishing institutional support and developing consensus regarding criteria by which to evaluate the contributions of advocacy activities to scholarship.

22 citations

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TL;DR: In this article, the authors provide guidance on how to write a policy brief by outlining four steps: (a) define the problem, (b) state the policy, (c) make your case, and (d) discuss the impact.
Abstract: Although many health care professionals are interested in health policy, relatively few have training in how to utilize their clinical experience and scientific knowledge to impact policy. Developing a policy brief is one approach that health professionals may use to draw attention to important evidence that relates to policy. This article offers guidance on how to write a policy brief by outlining 4 steps: (a) define the problem, (b) state the policy, (c) make your case, and (d) discuss the impact. The steps and tips offer a starting point for health care professionals interested in health policy and translating research or clinical experience to impact policy. (PsycINFO Database Record

12 citations


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TL;DR: This policy statement describes current knowledge on child poverty and the mechanisms by which poverty influences the health and well-being of children and describes the needs of pediatricians to address the social determinants of health when caring for children who live in poverty.
Abstract: Almost half of young children in the United States live in poverty or near poverty. The American Academy of Pediatrics is committed to reducing and ultimately eliminating child poverty in the United States. Poverty and related social determinants of health can lead to adverse health outcomes in childhood and across the life course, negatively affecting physical health, socioemotional development, and educational achievement. The American Academy of Pediatrics advocates for programs and policies that have been shown to improve the quality of life and health outcomes for children and families living in poverty. With an awareness and understanding of the effects of poverty on children, pediatricians and other pediatric health practitioners in a family-centered medical home can assess the financial stability of families, link families to resources, and coordinate care with community partners. Further research, advocacy, and continuing education will improve the ability of pediatricians to address the social determinants of health when caring for children who live in poverty. Accompanying this policy statement is a technical report that describes current knowledge on child poverty and the mechanisms by which poverty influences the health and well-being of children.

496 citations

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TL;DR: A transformative approach to foster collaboration across child health, public health, and community-based agencies to address the root causes of toxic stress and childhood adversity and to build community resilience is proposed.

162 citations

Journal ArticleDOI
TL;DR: A concept analysis is conducted, exploring the practical philosophical understanding of social responsibility and its implications for medical education and practice, to inform curricular development, professional practice, and further research on social responsibility.
Abstract: There is a growing demand for educating future physicians to be socially responsible. It is not clear, however, how social responsibility is understood and acted on in medical education and practice, particularly within the context of a growing desire to improve health care through an equitable and sustainable delivery system. The authors conduct a concept analysis, exploring the practical philosophical understanding of social responsibility and its implications for medical education and practice. The aim is to inform curricular development, professional practice, and further research on social responsibility. The particular ways in which social responsibility is interpreted can either enhance or establish limits on how it will appear across the continuum of medical education and practice. A physician's place in society is closely tied to a moral sense of responsibility related to the agreed-on professional characteristics of physicianhood in society, the capacity to carry out that role, and the circumstances under which such professionals are called to account for failing to act appropriately according to that role. The requirement for social responsibility is a moral commitment and duty developed over centuries within societies that advanced the notion of a "profession" and the attendant social contract with society. A curriculum focused on developing social responsibility in future physicians will require pedagogical approaches that are innovative, collaborative, participatory, and transformative.

152 citations

Journal ArticleDOI
TL;DR: Shen et al. as mentioned in this paper proposed a chatGPT model that optimizes language models for dialogue and showed that it is possible to train a few-shot language model with human preferences.
Abstract: HomeRadiologyVol. 307, No. 2 PreviousNext Reviews and CommentaryEditorialChatGPT and Other Large Language Models Are Double-edged SwordsYiqiu Shen , Laura Heacock, Jonathan Elias, Keith D. Hentel, Beatriu Reig, George Shih, Linda MoyYiqiu Shen , Laura Heacock, Jonathan Elias, Keith D. Hentel, Beatriu Reig, George Shih, Linda MoyAuthor AffiliationsFrom the Center for Data Science, New York University, 60 5th Ave, New York, NY 10011 (Y.S.); Department of Radiology, New York University School of Medicine, New York, NY (L.H., B.R., L.M.); and Departments of Primary Care (J.E.) and Radiology (K.D.H., G.S.), Weill Cornell Medicine, New York, NY.Address correspondence to Y.S. (email: [email protected]).Yiqiu Shen Laura HeacockJonathan EliasKeith D. HentelBeatriu ReigGeorge ShihLinda MoyPublished Online:Jan 26 2023https://doi.org/10.1148/radiol.230163See editorial bySom BiswasSee editorial byFelipe C. KitamuraMoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Min B, Ross H, Sulem E, et al. Recent advances in natural language processing via large pre-trained language models. arXiv 2111.01243 [preprint]. https://arxiv.org/abs/2111.01243. Posted November 1, 2021. Accessed January 19, 2023. Google Scholar2. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Advances in Neural Information Processing Systems 2017;30. https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html. Google Scholar3. ChatGPT: Optimizing Language Models for Dialogue. OpenAI. https://openai.com/blog/chatgpt/. Published November 30, 2022. Accessed January 19, 2023. Google Scholar4. Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems 2020;33:1877–1901.https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html. Google Scholar5. OpenAI Used Kenyan Workers on Less Than $2 Per Hour to Make ChatGPT Less Toxic. TIME. https://time.com/6247678/openai-chatgpt-kenya-workers/. Published January 18,2023. Accessed January 21, 2023. Google Scholar6. Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D. Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems 2017;30. https://papers.nips.cc/paper/2017/hash/d5e2c0adad503c91f91df240d0cd4e49-Abstract.html. Google Scholar7. Rohrbach A, Hendricks LA, Burns K, Darrell T, Saenko K. Object Hallucination in Image Captioning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018;4035–4045. Google Scholar8. Xiao Y, Wang WY. On Hallucination and Predictive Uncertainty in Conditional Language Generation. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume 2021. Google Scholar9. Clinical Decision Support. American Medical Informatics Association. https://amia.org/community/working-groups/clinical-decision-support#:~:text=Clinical%20Decision%20Support%20(CDS)%20is,care%20services%20and%20patient%20outcomes. Accessed January 16, 2023. Google Scholar10. Appropriate use criteria for advanced diagnostic imaging services. Code of Federal Regulations. https://www.ecfr.gov/current/title-42/chapter-IV/subchapter-B/part-414/subpart-B/section-414.94/. Accessed January 18, 2023. Google Scholar11. @tiktokrheumdok. ChatGPT to save time with Insurance Denials. TikTok. https://www.tiktok.com/@tiktokrheumdok/video/7176660771806383403. Published December 13, 2022. Accessed January 4, 2023. Google Scholar12. @StuartBlitz. You: There’s no ChatGPT use case in healthcare. Docs: Watch this. Twitter. https://twitter.com/StuartBlitz/status/1602834224284897282. Published December 13, 2022. Accessed January 13, 2023. Google Scholar13. ChatGPT FAQ: Commonly asked questions about ChatGPT. OpenAI. https://help.openai.com/en/articles/6783457-chatgpt-faq#:~:text=It%20has%20limited%20knowledge%20of%20world%20and%20events%20after%202021/. Accessed January 22, 2023. Google Scholar14. Reyes M, Meier R, Pereira S, et al. On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities. Radiol Artif Intell 2020;2(3):e190043. Link, Google Scholar15. Shen Y, Wu N, Phang J, et al. An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization. Med Image Anal 2021;68:101908. Crossref, Medline, Google Scholar16. Shen Y, Shamout FE, Oliver JR, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 2021;12(1):5645. Crossref, Medline, Google Scholar17. Shamout FE, Shen Y, Wu N, et al. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. NPJ Digit Med 2021;4(1):80. Crossref, Medline, Google Scholar18. Luft LM. The essential role of physician as advocate: how and why we pass it on. Can Med Educ J 2017;8(3):e109–e116. Crossref, Medline, Google Scholar19. Earnest MA, Wong SL, Federico SG. Perspective: Physician advocacy: what is it and how do we do it? Acad Med 2010;85(1):63–67. Crossref, Medline, Google Scholar20. Szakaly D. How ChatGPT Hijacks Democracy. New York Times. https://www.nytimes.com/2023/01/15/opinion/ai-chatgpt-lobbying-democracy.html. Published January 15, 2023. Accessed January 16, 2023. Google Scholar21. Report of the Select Committee on Intelligence, United States Senate, on Russian Active Measures Campaigns and Interference in the 2016 U.S. Election Volume 2: Russia’s Use of Social Media with Additional Views. https://www.intelligence.senate.gov/sites/default/files/documents/Report_Volume2.pdf. Accessed January 16, 2023. Google Scholar22. Huang K. Alarmed by A.I. Chatbots Universities Start Revamping How They Teach. New York Times. https://www.nytimes.com/2023/01/16/technology/chatgpt-artificial-intelligence-universities.html. Published January 16, 2023. Accessed January 19, 2023. Google Scholar23. Tian E. GPTZero https://gptzero.substack.com/. Published January 3, 2023. Accessed January 19, 2023. Google Scholar24. Gao CA, Howard FM, Markov NS, et al. Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers. bioRxiv 2022. https://doi.org/10.1101/2022.12.23.521610. Posted December 27, 2022. Accessed January 13, 2023. Google Scholar25. Bolton E, Hall D, Yasunaga M, Lee T, Manning C, Liang P. PubMedGPT 2.7B. Center for Research on Foundation Models, Stanford University. https://crfm.stanford.edu/2022/12/15/pubmedgpt.html. Accessed January 19, 2023. Google Scholar26. Clarification on Large Language Model Policy LLM. ICML 2023: Fortieth International Conference on Machine Learning. https://icml.cc/Conferences/2023/llm-policy. Accessed January 19, 2023. Google Scholar27. Bik EM, Casadevall A, Fang FC. The prevalence of inappropriate image duplication in biomedical research publications. MBio 2016;7(3):e00809–16. Crossref, Medline, Google Scholar28. Yue W, AbdAlmageed W, Natarajan P. ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019; 9543–9552. https://ieeexplore.ieee.org/document/8953774. Google Scholar29. Aditya R, Dhariwal P, Nichol A, Chu C, Chen M. Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv 2204.06125 [preprint]. https://arxiv.org/abs/2204.06125. Posted April 13, 2022. Accessed January 18, 2023. Google Scholar30. Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-resolution image synthesis with latent diffusion models. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, June 2022. Google Scholar31. Chambon P, Bluethgen C, Langlotz CP, Chaudhari A. Adapting pretrained vision-language foundational models to medical imaging domains. arXiv 2210.04133 [preprint]. https://arxiv.org/abs/2210.04133. Posted October 9, 2022. Accessed January 18, 2023. Google Scholar32. Biswas S. ChatGPT and the Future of Medical Writing. Radiology 2023;307(2):e223312. Link, Google Scholar33. Kitamura FC. ChatGPT Is Shaping the Future of Medical Writing But Still Requires Human Judgment. Radiology 2023;307(2):e230171. Link, Google ScholarArticle HistoryReceived: Jan 23 2023Revision requested: Jan 23 2023Revision received: Jan 23 2023Accepted: Jan 23 2023Published online: Jan 26 2023 FiguresReferencesRelatedDetailsCited ByChatGPT Is Shaping the Future of Medical Writing But Still Requires Human JudgmentFelipe C. Kitamura, 2 February 2023 | Radiology, Vol. 307, No. 2The Role and Limitations of Large Language Models Such as ChatGPT in Clinical Settings and Medical JournalismFurkan Ufuk, 7 March 2023 | Radiology, Vol. 0, No. 0The potential impact of ChatGPT in clinical and translational medicineVivian WeiwenXue, PingguiLei, William C.Cho2023 | Clinical and Translational Medicine, Vol. 13, No. 3Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?Ian L.Alberts, LorenzoMercolli, ThomasPyka, GeorgePrenosil, KuangyuShi, AxelRominger, AliAfshar-Oromieh2023 | European Journal of Nuclear Medicine and Molecular ImagingTransformers, codes and labels: large language modelling for natural language processing in clinical radiologyDenisRemedios, AlexRemedios2023 | European RadiologyChatGPT—a foe or an ally?Om PrakashYadava2023 | Indian Journal of Thoracic and Cardiovascular SurgeryChatGPTDiveshSardana, Timothy R.Fagan, John TimothyWright2023 | The Journal of the American Dental AssociationThe promise and peril of ChatGPT in geriatric nursing education: What We know and do not knowXiangQi, ZhengZhu, BeiWu2023 | Aging and Health Research, Vol. 3, No. 2Authors in the Age of Language-generation AI: To be or not to be, is that Really the Question?José DaríoMartínez-Ezquerro2023 | Archives of Medical ResearchBeyond chatting: The opportunities and challenges of ChatGPT in medicine and radiologyJuan M. LavistaFerres, William B.Weeks, Linda C.Chu, Steven P.Rowe, Elliot K.Fishman2023 | Diagnostic and Interventional ImagingAttention is not all you need: the complicated case of ethically using large language models in healthcare and medicineStefanHarrer2023 | eBioMedicine, Vol. 90Chatting or cheating? The impacts of ChatGPT and other artificial intelligence language models on nurse educationEdmond Pui HangChoi, Jung JaeLee, Mu-HsingHo, Jojo Yan YanKwok, Kris Yuet WanLok2023 | Nurse Education Today, Vol. 125Is ChatGPT a valid author?Jaime A.Teixeira da Silva2023 | Nurse Education in Practice, Vol. 68Do Large Language Models Understand Chemistry? A Conversation with ChatGPTCayque MonteiroCastro Nascimento, André SilvaPimentel2023 | Journal of Chemical Information and Modeling, Vol. 63, No. 6Readership Awareness Series – Paper 4: Chatbots and ChatGPT - Ethical Considerations in Scientific PublicationsMohammad JavedAli, AliDjalilian2023 | Seminars in OphthalmologyA SWOT analysis of ChatGPT: Implications for educational practice and researchMohammadrezaFarrokhnia, Seyyed KazemBanihashem, OmidNoroozi, ArjenWals2023 | Innovations in Education and Teaching InternationalChatGPT and other artificial intelligence applications speed up scientific writingTzeng-JiChen2023 | Journal of the Chinese Medical Association, Vol. 86, No. 4ChatGPT for tourism: applications, benefits and risksInêsCarvalho, StanislavIvanov2023 | Tourism ReviewImplications of large language models such as ChatGPT for dental medicineFlorinEggmann, RolandWeiger, Nicola U.Zitzmann, Markus B.Blatz2023 | Journal of Esthetic and Restorative DentistryAI and GPT for Management Scholars and Practitioners: Guidelines and ImplicationsSudhirRana2023 | FIIB Business Review, Vol. 12, No. 1Can ChatGPT improve communication in hospitals?DavidSantandreu-Calonge, PabloMedina-Aguerrebere, PatrikHultberg, Mariam-AmanShah2023 | El Profesional de la informaciónChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid ConcernsMalikSallam2023 | Healthcare, Vol. 11, No. 6Future Speech Interfaces with Sensors and Machine IntelligenceBruceDenby, Tamás GáborCsapó, MichaelWand2023 | Sensors, Vol. 23, No. 4Editorial: The Use of Artificial Intelligence (AI)-Assisted Technologies in Scientific DiscourseArvieVitente, RolandoLazaro, Catherine JoyEscuadra, JocelRegino, EsmeritaRotor2023 | Philippine Journal of Physical TherapyChatGPT and Big Data: Enhancing Text-to-Speech ConversionHatim AbdelhakDida, DSKChakravarthy, FazleRabbi2023 | Mesopotamian Journal of Big DataAn era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challengesAbidHaleem, MohdJavaid, Ravi PratapSingh2022 | BenchCouncil Transactions on Benchmarks, Standards and Evaluations, Vol. 2, No. 4Artificial intelligence and dental researchSMBalaji2022 | Indian Journal of Dental Research, Vol. 33, No. 4Accompanying This ArticleChatGPT and the Future of Medical WritingFeb 2 2023RadiologyChatGPT Is Shaping the Future of Medical Writing But Still Requires Human JudgmentFeb 2 2023RadiologyChatGPT- Special Radiology:AI Podcast CollaborationFeb 21 2023Default Digital Object SeriesEpisode 17: ChatGPT- Special Radiology Podcast CollaborationFeb 21 2023Default Digital Object SeriesRecommended Articles Deep Generative Adversarial Networks: Applications in Musculoskeletal ImagingRadiology: Artificial Intelligence2021Volume: 3Issue: 3Deep Learning: A Primer for RadiologistsRadioGraphics2017Volume: 37Issue: 7pp. 2113-2131Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural NetworksRadiology2017Volume: 284Issue: 2pp. 574-582Convolutional Neural Networks for Radiologic Images: A Radiologist’s GuideRadiology2019Volume: 290Issue: 3pp. 590-606See More RSNA Education Exhibits Artificial Intelligence in Diagnostic Imaging: Current Applications and Future PerspectiveDigital Posters2019Artificial Intelligence in Breast Imaging: Past, Present, and FutureDigital Posters2020Generative Adversarial Networks (GANs): A Primer for RadiologistsDigital Posters2019 RSNA Case Collection Dynamic MRI findings of COVID-19 pneumoniaRSNA Case Collection2020Slow-growing cancerRSNA Case Collection2020Creutzfeldt-Jakob DiseaseRSNA Case Collection2021 Vol. 307, No. 2 PodcastPodcastMetrics Altmetric Score PDF download

151 citations

Journal ArticleDOI
TL;DR: Aligning leadership curricula with competency models, such as the MLCF, would create opportunities to standardize evaluation of outcomes, leading to better measurement of student competency and a better understanding of best practices.
Abstract: Purpose To characterize leadership curricula in undergraduate medical education as a first step toward understanding best practices in leadership education. Method The authors systematically searched the PubMed, Education Resources Information Center, Academic Search Complete, and Education Full Text databases for peer-reviewed English-language articles published 1980-2014 describing curricula with interventions to teach medical students leadership skills. They characterized educational settings, curricular format, and learner and instructor types. They assessed effectiveness and quality of evidence using five-point scales adapted from Kirkpatrick's four-level training evaluation model (scale: 0-4) and a Best Evidence Medical Education guide (scale: 1-5), respectively. They classified leadership skills taught into the five Medical Leadership Competency Framework (MLCF) domains. Results Twenty articles describing 24 curricula met inclusion criteria. The majority of curricula (17; 71%) were longitudinal, delivered over periods of one semester to four years. The most common setting was the classroom (12; 50%). Curricula were frequently provided to both preclinical and clinical students (11; 46%); many (9; 28%) employed clinical faculty as instructors. The majority (19; 79%) addressed at least three MLCF domains; most common were working with others (21; 88%) and managing services (18; 75%). The median effectiveness score was 1.5, and the median quality of evidence score was 2. Conclusions Most studies did not demonstrate changes in student behavior or quantifiable results. Aligning leadership curricula with competency models, such as the MLCF, would create opportunities to standardize evaluation of outcomes, leading to better measurement of student competency and a better understanding of best practices.

115 citations