Application of Survival Analysis in Forecasting Medical Students at Risk

Autor/in	Ghasemi, Abolfazl
Titel	Application of Survival Analysis in Forecasting Medical Students at Risk
Quelle	(2018), (162 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, Ohio University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	979-8-3635-2527-8
Schlagwörter	Hochschulschrift; Dissertation; Medical Students; Medical Education; At Risk Students; Gender Differences; Student Characteristics; Licensing Examinations (Professions); Academic Achievement; Academic Failure; First Generation College Students; In State Students; Age Differences; Race; Ethnicity; Undergraduate Study; Science Achievement; Models; Prediction; College Entrance Examinations; Medical Schools; Grade Point Average; Medical College Admission Test + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Medizinische Ausbildung; Geschlechterkonflikt; Schulleistung; Age; Difference; Age difference; Altersunterschied; Rasse; Abstammung; Ethnizität; Grundstudium; Analogiemodell; Vorhersage; Aufnahmeprüfung
Abstract	The purpose of this study was to build a forecasting model for medical students at risk using the survival analysis technique. Authors of previous studies have investigated dropouts from medical programs or success of medical students on national board exams. However, little research has been done to identify students at risk, mainly the timing of failure and the risk factors surrounding this situation. The theoretical framework of this study, which considers psychological, sociological, and organizational elements that affect students' success or failure, were reviewed. The Cox regression model was used. The findings of this study indicated that gender, being the first generation, and being in-state are not risk factors for medical students. However, age, race/ethnicity, undergraduate science GPA, and MCAT subscores (specifically in biology, physics, and verbal) are the risk factors that can be incorporated into the forecasting model. The robustness of the forecasting model was evaluated by running resampling 10,000 times through the bootstrapping technique. Hazard ratios remained the same, and no significant changes occurred. The results of this study support a comprehensive prevention program by using historical data from pre-admissions variables and timing of failures records, and building a forecasting model as the proper strategy to proactively target students at risk. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Update	2024/1/01