References
- Chan T, Sebok-Syer S, Thoma B, et al. Learning analytics in medical education assessment: the past, the present, and the future. AEM Educ Train. 2018; 2(2): 178–87. DOI: 10.1002/aet2.10087
- Acai A, Cupido N, Weavers A, et al. Competence committees: The steep climb from concept to implementation. Med Educ. 2021; 55(9): 1067–77. DOI: 10.1111/medu.14585
- Abbott KL, George BC, Sandhu G, et al. Natural language processing to estimate clinical competency committee ratings. J Surg Educ. 2021; 78(6): 2046–51. DOI: 10.1016/j.jsurg.2021.06.013
- Yilmaz Y, Jurado Nunez A, Ariaeinejad A, et al. Harnessing natural language processing to support decisions around workplace-based assessment: Machine learning study of competency-based medical education. JMIR Med Educ. 2022; 8(2):
e30537 . DOI: 10.2196/30537 - Zhang R, Pakhomov S, Gladding S, et al. Automated assessment of medical training evaluation text. AMIA Annu Symp Proc. 2013; 1459–68.
- Stahl CC, Jung SA, Rosser AA, et al. Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy. Am J Surg. 2021; 221(2): 369–75. DOI: 10.1016/j.amjsurg.2020.11.044
- Gin BC, Ten Cate O, O’Sullivan PS, Hauer KE, Boscardin C. Exploring how feedback reflects entrustment decisions using artificial intelligence. Med Educ. 2022; 56(3): 303–11. DOI: 10.1111/medu.14696
- Rojek AE, Khanna R, Yim JWL, et al. Differences in narrative language in evaluations of medical students by gender and under-represented minority status. J Gen Intern Med. 2019; 34(5): 684–91. DOI: 10.1007/s11606-019-04889-9
- O’Brien CL, Sanguino SM, Thomas JX, Green MM. Feasibility and outcomes of implementing a portfolio assessment system alongside a traditional grading system. Acad Med. 2016; 91(11): 1554–60. DOI: 10.1097/ACM.0000000000001168
- O’Brien CL, Thomas JX, Green MM. What is the relationship between a preclerkship portfolio review and later performance in clerkships? Acad Med. 2018; 93(1): 113–8. DOI: 10.1097/ACM.0000000000001771
- Hanson JL, Rosenberg AA, Lane JL. Narrative descriptions should replace grades and numerical ratings for clinical performance in medical education in the United States. Front Psychol. 2013; 4: 668. DOI: 10.3389/fpsyg.2013.00668
- Ginsburg S, van der Vleuten CPM, Eva KW. The hidden value of narrative comments for assessment: a quantitative reliability analysis of qualitative data. Acad Med. 2017; 92(11): 1617–21. DOI: 10.1097/ACM.0000000000001669
- Ginsburg S, van der Vleuten C, Eva KW, Lingard L. Hedging to save face: a linguistic analysis of written comments on in-training evaluation reports. Adv Health Sci Educ Theory Pr. 2016; 21(1): 175–88. DOI: 10.1007/s10459-015-9622-0
- Ginsburg S, Kogan JR, Gingerich A, Lynch M, Watling CJ. Taken out of context: hazards in the interpretation of written assessment comments. Acad Med. 2020; 95(7): 1082–8. DOI: 10.1097/ACM.0000000000003047
- Grimmer J, Roberts ME, Stewart, BM.
Text as Data: a new framework for machine learning and the social sciences . Princeton, NJ: Princeton University Press; 2022. - Goth G. Deep or shallow, NLP is breaking out. Commun Acm. 2016; 59(3): 13–6. DOI: 10.1145/2874915
- Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res. 2003; 3(4–5): 993–1022. DOI: 10.1162/jmlr.2003.3.4-5.993
- Creswell JW, Poth CN.
Qualitative inquiry & research design: choosing among five approaches . Fourth edition. Los Angeles: SAGE; 2018. - Ginsburg S, Regehr G, Lingard L, Eva KW. Reading between the lines: faculty interpretations of narrative evaluation comments. Med Educ. 2015; 49(3): 296–306. DOI: 10.1111/medu.12637
