[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-205407-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-205407-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","struggling-with-strugglers-using-data-from-selection-tools-for-early-identification-of-medical-students-at-risk-of-failure","Struggling with Strugglers - Using Data from Selection Tools for Early Identification of Medical Students at Risk of Failure","","Medical education research addresses student selection, yet struggling learners remain under-researched despite the importance of early identification for effective remediation. This study evaluates whether pre-admission medical school admission tools can predict academic struggle among admitted students in a six-year undergraduate program. Using data from 700 students, Discriminate Function Analysis tests pre-admission achievement, UMAT, and interview scores. Pre-admission academic achievement (ATAR/GPA) predicts struggling most strongly, while UMAT and interview contribute comparatively less.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/struggling-with-strugglers-using-data-from-selection-tools-for-early-identification-of-medical-students-at-risk-of-failure/205407/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/struggling-with-strugglers-using-data-from-selection-tools-for-early-identification-of-medical-students-at-risk-of-failure/205407.png","ImageObject",300,407,{"name":42,"@type":43},"Gloria","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-05",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the study address regarding medical student selection?","Question",{"text":63,"@type":64},"It focuses on struggling medical students, a group less addressed in selection research that often emphasizes applicants likely to excel rather than those likely to fail.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were students classified as “struggling” in this study?",{"text":68,"@type":64},"Students were classified as struggling if they failed any end-of-phase examination but still graduated during the six-year program.",{"name":70,"@type":61,"acceptedAnswer":71},"Which admission tool best predicted academic struggle?",{"text":72,"@type":64},"Pre-admission academic achievement, measured as ATAR or GPA, was the strongest positive predictor, whereas UMAT and interview scores had comparatively smaller predictive effects.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},205407,1788579257,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":102,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":80,"read_time":143,"preview_page_count":4},2336474459895,"https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916","Li et al. BMC Medical Education (2019) 19:415 [https://doi.org/10.1186/s12909-019-1860-z](https://doi.org/10.1186/s12909-019-1860-z)  \nRESEARCH ARTICLE Open Access   \nStruggling with strugglers: using data from selection tools for early identification of medical students at risk of failure  \nJames Li 1, Rachel Thompson2 and Boaz Shulruf2*  \nAbstract  \nBackground: Struggling medical students is an under-researched in medical education. It is known, however, that early identification is important for effective remediation. The aim of the study was to determine the predictive effect of medical school admission tools regarding whether a student will struggle academically.  \nMethods: Data comprise 700 students from the University of New South Wales undergraduate medical program. The main outcome of interest was whether these students struggled during this 6-year program; they were classified to be struggling they failed any end-of-phase examination but still graduated from the program. Discriminate Function Analysis (DFA) assessed whether their pre-admission academic achievement, Undergraduate Medicine Admission Test (UMAT) and interview scores had predictive effect regarding likelihood to struggle. Results: A lower pre-admission academic achievement in the form of Australian Tertiary Admission Rank (ATAR) or Grade Point Average (GPA) were found to be the best positive predictors of whether a student was likely to struggle. Lower UMAT and poorer interview scores were found to have a comparatively much smaller predictive effect.  \nConclusion: Although medical admission tests are widely used, medical school rarely use these data for educational purposes. The results of this study suggest admission test data can predict who among the admitted students is likely to struggle in the program. Educationally, this information is invaluable. These results indicate that pre-admission academic achievement can be used to predict which students are likely to struggle in an Australian undergraduate medicine program. Further research into predicting other types of struggling students as well as remediation methods are necessary.  \nKeywords: Medical education, Undergraduate, Australia, Prediction, Medical student  \nBackground  \nResearch into medical student selection often focuses on prediction models for identifying applicants who would excel as students and less so on those who are likely to fail [1]. However, there is a group of students known as‘strugglers’ who fall into neither of these categories. These struggling students are the ones who encounter academic and/or personal difficulties, which causes them to struggle throughout their study in a medical program [1]. Whilst this is a broad categorisation, often the  \n* Correspondence: [b.shulruf@unsw.edu.au](b.shulruf@unsw.edu.au)  \n2Office of Medical Education, University of New South Wales, Sydney, Australia  \nFull list of author information is available at the end of the article  \nliterature defines such groups differently to one another and uses varying names for the students, such as ‘problem learners’, ‘at-risk students’, ‘disruptive students’, and‘marginal students’ [2, 3]. The prevalence of strugglers is rarely reported but is believed to lie in the range of 10– 15% among undergraduate medical programs [2–4], though rates as high as 36.6–47.3% of students have been reported to have had impeded progress through their medical course [5]. This number could potentially be explained by the usage of different definitions or the fact that struggling students are often unidentified, or poorly reported [2, 6]. These students often report lower motivation, increased frustration and boredom, and lower confidence in their ability to learn [3, 7].  \n© The Author(s) . 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4 .0 International License ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)), which permits unre","cbCaic4aiOX5FJ8g","https://ap.wps.com/l/cbCaic4aiOX5FJ8g","pdf",431887,"English","# Background\n## Challenges in identifying struggling students\n# Methods\n## Study population and outcome definition\n## Predictors and statistical approach\n# Results\n## Predictive effect of admission tools\n# Conclusion\n## Educational implications and future research","[{\"question\":\"What problem does the study address regarding medical student selection?\",\"answer\":\"It focuses on struggling medical students, a group less addressed in selection research that often emphasizes applicants likely to excel rather than those likely to fail.\"},{\"question\":\"How were students classified as “struggling” in this study?\",\"answer\":\"Students were classified as struggling if they failed any end-of-phase examination but still graduated during the six-year program.\"},{\"question\":\"Which admission tool best predicted academic struggle?\",\"answer\":\"Pre-admission academic achievement, measured as ATAR or GPA, was the strongest positive predictor, whereas UMAT and interview scores had comparatively smaller predictive effects.\"}]","Struggling with Strugglers - Using Data from Selection Tools for Early Identification of Medical Students at Risk of Failure | PDF",15]