Undergraduate GPA Research Articles

The predictive validity of MCAT scores and undergraduate GPA for COMLEX-USA licensure exam performance of students enrolled in osteopathic medical schools.

Osteopathic (Doctor of Osteopathic Medicine [DO]) medical students account for more than 25 % of all medical students in the United States. This study examined the predictive validity of Medical College Admission Test (MCAT) total scores and cumulative undergraduate grade point averages (UGPAs) for performance on the Comprehensive Osteopathic Medical Licensing Examination of the United States (COMLEX-USA) Level 1 and Level 2-CE (Cognitive Evaluation) licensure examinations administered by the National Board of Osteopathic Medical Examiners (NBOME). Additionally, the study examined the degree to which MCAT total scores and UGPAs provide comparable prediction of student performance by key sociodemographic variables. This study involved a collaborative effort between the Association of American Medical Colleges (AAMC), the American Association of Colleges of Osteopathic Medicine (AACOM) and the NBOME. Data were examined for 39 accredited DO-granting medical schools in the United States during the 2017 application cycle. Researchers utilized three regression models that included MCAT total scores, cumulative UGPA, and combined MCAT total scores and cumulative UGPA to determine predictive validity. Researchers also examined the comparability of prediction for sociodemographic variables by examining the differences between observed and predicted error for both scores and pass/fail success rates. Medium to large correlations were discernible between MCAT total scores, UGPA, and COMLEX-USA examination outcomes. For both COMLEX-USA Level 1 and Level 2-CE scores and pass/fail outcomes, MCAT scores alone provided superior predictive value to UGPA alone. However, MCAT scores and UGPA utilized in conjunction provided the best predictive value. When predicting both licensure examination scores and pass/fail outcomes by sociodemographic variables, all three models provided comparable predictive accuracy. Findings from this comprehensive study of DO-granting medical schools provide evidence for the value-added benefit of taking MCAT scores and UGPA into consideration, particularly when these measures are utilized in conjunction. Further, findings provide evidence indicating that individuals from different sociodemographic backgrounds who enter medical school with similar MCAT scores and UGPA perform similarly on licensure examination outcome measures.

An Exploration of Factors Influencing the Passing of Mainland Chinese Students' Study Abroad Applications Based on Logistic Regression Modeling--Example of Applying for Master's Degree in Hong Kong

In order to explore the admission factors of mainland Chinese students applying for master's degree in Hong Kong, the article collected about 240 data by visiting and researching professional study abroad application agencies and students in colleges and universities. It collected undergraduate institution background, major grades, target institution ranking, language scores and undergraduate grades of the applicants and analyzed and explored them based on the logistic regression model. We found that undergraduate institutional rank, undergraduate GPA, target institution rank, and major match were significantly different across admissions outcomes and were able to conclude, that undergraduate institutional rank, GPA, and target institution rank had the most significant effect on admissions outcomes, and that language scores and major match did not significantly affect admissions outcomes. We hypothesize that there may be a survivor bias in the data collected, and that some unquantifiable factors of soft backgrounds such as research competitions, internships, and patents and journals may have an impact on the admissions outcomes. We also provide strategies to improve the success rate of the application.

A Statistical Analysis of GRE/GMAT Data for Admission to Master’s Degree Programs

In this paper, we investigate the waiving of GRE/GMAT for admission to master’s degree programs in a state university in Florida, USA. Standardized tests, such as GRE/GMAT, were required for admission to the master’s degree programs in 2019/2020, waived in 2020/2021, and removed or modified in 2021/2022. We analyzed the application, enrollment, and performance data to assess the impact of these changes. The data show that the number of applicants and enrolled students exhibit an upward trend from 2019 to 2021. The undergraduate GPA of new applicants who did not submit the GRE in 2021 tends to be statistically significantly higher than for those who did submit the GRE in 2019 (p < 0.001). The new students’ first-semester graduate GPA in 2021 (no GRE requirement) tends also to be statistically significantly higher than the new students’ first-semester graduate GPA in 2019 (GRE requirement) (p < 0.01). The study employed random forest feature importance using the Gini index to analyze the predictive power of GRE and undergraduate GPA for forecasting first-semester graduate GPA. The results show that undergraduate GPA is a more significant factor than GRE. Overall, the study’s statistical evidence indicates that waiving GRE/GMAT requirements for master’s degree programs did not affect applicants’ performance, as measured by their undergraduate GPA, nor did it lead to a decline in student performance, as measured by first-semester graduate GPA.

Exploring Undergraduate Latine Students’ Learning Experiences in STEM Courses

ABSTRACT We used a phenomenological research design to explore 19 Latina and Queer identifying undergraduate students’ lived experiences in STEM undergraduate courses at a Hispanic-Serving Institution (HSI). Eighteen participants self-identified as female and one identified as genderqueer with an average undergraduate grade point average of 3.07. The following themes emerged from phenomenological data analysis: failure to acknowledge students’ cultural and language resources; deficit assumptions about students’ background, abilities, and potential; absence of active and collaborative learning and practical and real-world examples; minimal feedback on student learning and growth; disregard for student voices in design of assessments; and misalignment between assessments and evaluation tools to measure those assessments. We provide implications for faculty developers at HSIs and other institutions of higher education.

Impact of Masking Academic Metrics on Enrolling a Broadly Diverse and Mission-Aligned Student Body.

The June 2023 U.S. Supreme Court decision overturning affirmative action required medical schools to discontinue considering race/ethnicity in admissions decisions. Medical schools must now identify different strategies if they aim to recruit and admit applicants from groups underrepresented in medicine (URiM; race/ethnicity), as enrolling broadly diverse students remains critical for serving the U.S. population. Washington State University Elson S. Floyd College of Medicine (established in 2015) has an admissions process that assesses academic metrics using national threshold combinations of undergraduate grade point averages (UGPAs) and Medical College Admission Test (MCAT) scores (published on school's website), and legal residency in or ties to Washington state, as prescreening criteria for secondary applications. UGPAs and MCAT scores are then masked from further consideration, allowing for decisions to be made with a focus on mission-aligned criteria, such as certain personal attributes and lived experiences and coming from specific environments (i.e., educationally or socioeconomically disadvantaged backgrounds, rural communities, military service, or a member of a federally recognized Tribe). In the last 5 admissions cycles (enrollment years 2018-2022), cohort data demonstrates that as the admissions funnel narrows and each subsequent pool is smaller than the preceding one, the representation of mission-aligned applicants increases, despite the masking of academic metrics. The most recently enrolled class (enrollment year 2022) of 80 had 14 (17.5%) URiM students, closely mirroring the state's general population. The overall yield (acceptance:matriculation) has steadily improved with the last 2 cycles to 1.68:1 and 1.65:1, indicating slightly more than 1.5 times the number of offers needed to fill the class are being made. Next steps include further refining the process by considering more granular data on applicants' childhood community characteristics and rural background and examining how admissions data may correlate with residency and practice location and communities served.

Examining the Early Academic Performance of Doctor of Physical Therapy Students After Holistic Review: A Retrospective Cohort Study.

A diverse health care workforce can help to address systemic health disparities. Holistic review and use of an expanded definition of underrepresented minority (URM) that includes factors beyond race and ethnicity are suggested methods to promote diversity in the physical therapist (PT) workforce and ultimately address societal health disparities. Evidence suggests that holistic review can promote PT student diversity; however, limited evidence exists that describes the academic outcomes of holistic review that consider social determinants of learning (SDOL) factors in the review process. The purpose of this study was to examine the early academic outcomes of students after a holistic review and to examine relationships between SDOL factors and academic performance. A convenience sample of 160 students admitted to a doctor of physical therapy (DPT) program from June 2020 to June 2022 was used. A secondary analysis of students' self-reported responses from their admissions application was used to categorize response data into SDOL groups. An independent-sample t-test was used to test group differences in academic performance, and regression modeling was used to test relationships between SDOL factors and academic performance. The mean 6-month DPT program grade point average (GPA) of the sample was 3.32 ± .43. Nonstatistically significant differences appeared in early DPT GPA between URM and non-URM student groups. However, after controlling for undergraduate GPA, a history of educational disadvantage was the only SDOL factor that contributed to early DPT GPA variance (2.6%), P < .05. The similar academic performance of URM and non-URM students provides support for using an expanded URM definition in holistic review. Most of the educationally disadvantaged group were first-generation college students, which was associated with lower early GPA. These results provide emerging evidence that first-generation students in DPT programs may benefit from additional academic support.

A meta-analysis of the relationship between Wonderlic test scores and school success

This meta-analysis examined the validity of an alternative to traditional assessments called the Wonderlic which is a brief measure of general mental ability. Our results showed significant, positive correlations between Wonderlic scores and academic performance in general ( r̅ = .26), between Wonderlic scores and undergraduate GPA in particular ( r̅ = .27, ρ ¯ = .33), and between Wonderlic scores and retention ( r̅ =.09, ρ ¯ = .12). We also identified several significant moderators of the relationship between Wonderlic scores and relevant outcomes (e.g., test publisher reported coefficients were larger than those reported by other sources). Subgroup differences in test scores were in the same range as other post-secondary admissions assessments (e.g., ACT and SAT scores). Overall, the Wonderlic has similar levels of subgroup differences and is less strongly related to GPA than traditional assessments but still retains useful levels of predictiveness and is a shorter, less expensive assessment that requires less preparation than the ACT or SAT.

Graduate Admissions in Psychology Before and After the Pandemic: GRExit Comes to Psychology

Background Admission to graduate school in psychology is competitive. It is essential for both undergraduate students who seek graduate training and their professors to understand the key factors that contribute to successful applications for graduate study. Objective We aimed to see how the global COVID-19 pandemic affected graduate admissions decisions by comparing the results from nationwide surveys distributed in 2014 and 2022. Method A Qualtrics script asked respondents to rate the importance of 16 factors in admission to graduate school in psychology by reporting a percentage value for each factor, adding up to 100% across all factors. Results The importance of factors that had figured prominently in the initial sample (e.g., research experience, undergraduate GPA, recommendation letters, and personal statement) increased or remained stable in the recent sample, but all versions of the Graduate Record Exam decreased in importance. Conclusions Students who aspire to graduate study in psychology should engage in research and disseminate the results in papers and presentations. Teaching Implications Professors involved in training undergraduate psychology students need to provide opportunities for their students to engage in research, and to find outlets for the students to disseminate their findings such as undergraduate journals and regional conferences.

Exam evaluation in prosthodontics across preclinical and clinical years from students' perspective: A cross-sectional study.

The purpose of this study was to explore the students' perceptions and performance in prosthodontics theory exam. A cross-sectional descriptive study was conducted on 560 (80.82%) students of different levels (third, fourth and fifth years) to explore their opinions and performance with regard to a number of issues on a prosthodontics theory exam (exam evaluation, exam preparation, exam material, exam timing). Demographic data were also collected. Descriptive statistics were generated and Chi-square test, independent sample t-test, ANOVA test and Pearson's correlation coefficient were used to examine the associations between different variables. The significance level was set at p < .05. Students' responses regarding exam evaluation was influenced by their gender, study level, high-school Grade Point Average (GPA) and undergraduate cumulative GPA. Perceived exam difficulty was significantly affected by gender (p = .03) and study level (p < .001), and negatively correlated to both high-school GPA (p < .001) and university GPA (p = .03). The vast majority (88.2%) depended on lecture hand-outs and lecture notes for study. Exam material and preparation were not significantly affected by any of the demographic variables with most respondents (76.8%) thinking that the lectures blended with prosthodontics laboratories/clinics would improve their understanding of the exam material. The suggested best time to conduct the exam was early afternoon (31.6%). Student performance was significantly affected by the study level (p < .001) and cumulative GPA (p < .001) with significant positive correlation between the high-school GPA and the mark in the exam (r = .29, p < .001) and by the amount of time students spent for exam preparation (p < .001). Those students who reported using textbooks to prepare for the exam got significantly higher marks (66.1 ± 8.7) compared to the students who did not (62.8 ± 9.7) (p = .03). Course level, GPA and gender were identified as the most influential factors in different aspects of exam evaluation and students' performance. Regular study and use of textbooks were demonstrated to improve academic performance. Additional orientation and guidance relating to the exam (especially for third year students) would be welcomed, as would alternate teaching methods such as small group discussions or study groups.

Assessing the impact of adopting a competency-based medical education framework and ACGME-I accreditation on educational outcomes in a family medicine residency program in Abu Dhabi Emirate, United Arab Emirates.

Competency-Based Medical Education (CBME) is now mandated by many graduate and undergraduate accreditation standards. Evaluating CBME is essential for quantifying its impact, finding supporting evidence for the efforts invested in accreditation processes, and determining future steps. The Ambulatory Healthcare Services (AHS) family medicine residency program has been accredited by the Accreditation Council of Graduate Medical Education-International (ACGME-I) since 2013. This study aims to report the Abu Dhabi program's experience in implementing CBME and accreditation. Compare the two residents' cohorts' performance pre-and post-ACGME-I accreditation.Study the bi-annually reported milestones as a graduating residents' performance prognostic tool. All residents in the program from 2008 to 2019 were included. They are called Cohort one-the intake from 2008 to 2012, before the ACGME accreditation, and Cohort two-the intake from 2013 to 2019, after the ACGME accreditation, with the milestones used. The mandatory annual in-training exam was used as an indication of the change in competency between the two cohorts. Among Cohort two ACGME-I, the biannual milestones data were studied to find the correlation between residents' early and graduating milestones. A total of 112 residents were included: 36 in Cohort one and 76 in Cohort two. In Cohort one, before the ACGME accreditation, no significant associations were identified between residents' graduation in-training exam and their early performance indicators, while in Cohort two, there were significant correlations between almost all performance metrics. Early milestones are correlated with the graduation in-training exam score. Linear regression confirmed this relationship after controlling the residents' undergraduate Grade Point Average (GPA). Competency development continues to improve even after residents complete training at Post Graduate Year, PGY4, as residents' achievement in PGY5 continues to improve. Improved achievement of residents after the introduction of the ACGME-I accreditation is evident. Additionally, the correlation between the graduation in-training exam and graduation milestones, with earlier milestones, suggests a possible use of early milestones in predicting outcomes.

Measuring 'Up': The Promise of Undergraduate GPA Growth in Law School Admissions

Measuring 'Up': The Promise of Undergraduate GPA Growth in Law School Admissions

Admission Factors and Academic Achievements Associated with the Risk of Grade Retention and National Exam Failure in the School of Dentistry

Objectives: The purpose of this study was to evaluate the risk factors associated with grade retention and national exam failure in the school of dentistry.Methods: The subjects of this study were students who enrolled at the school of dentistry from 2013-2017. Univariable and multivariable analyses were examined through logistic regression analysis. The potential contributing factors of grade retention and national exam failure include: graduated high school, graduated college, major, admission score, admission type, decision type, gender, age at the time of admission, and dental school Grade Point Average(GPA).Results: Higher dental school score correlated with lower risk of grade retention or failing national exam, but undergradu-ate GPA was not related to the risk of national exam failure. Older students and specific admission types had higher risk of repeating school year.Conclusion: It's important to provide focused guidance for high-risk students, especially those with average of dental school score below 80, to prevent grade retention or national exam failure. Developing a student achievement monitoring system can help prevent grade retention or national exam failure.

Implications of Student Loan and Finance Management Skills for Undergraduate Students

As the Presidential elections in 2024 are approaching, public opinions and politicians’ statements about student loans are presented more often. To curb educational borrowing, the entire society is involved. Institutions of higher education are highly engaged in securing possible funds for scholarships and grants to provide their students with the best and most efficient education. States offer scholarships, and students are becoming more selective about their institution, study major, and lifestyle choices during their undergraduate studies. Many borrowers lack a clear understanding of loan terms and finance management skills. This study was conducted to determine the money management skills of undergraduates, their perceived amount of educational loans anticipated upon graduation, and its impact on their academic performance. A specifically designed survey instrument was administered to a total of 565 participants who were students at one of the US HBCUs in the North-Eastern region of the United States. The results revealed significant race differences in the perceived expected amount of debt upon graduation. The study further indicates a heightened requirement for academic guidance for students whose undergraduate GPAs fall within the range of 2.00 to 3.00, aiming to enhance their retention until graduation within the present institution. Student athletes expressed a significantly higher financial selfefficacy than non-athletes.

Predicting Medical Student Performance With a Situational Judgment Test in Admissions.

To explore correlations between AAMC situational judgment test (SJT) scores, other admissions data, and learners' medical school performance. First- and second-year medical students from 8 U.S. MD-granting medical schools completed a prototype version of the AAMC SJT in 2017. Outcomes included research-only faculty ratings of student performance, final course grades, and faculty evaluations of student performance, 2017-2018 and 2018-2019 academic years. Bivariate correlations were used to investigate the relationship between SJT scores and student performance outcomes and hierarchical regressions to investigate whether SJT scores provided incremental validity over MCAT total scores and cumulative undergraduate grade point averages (UGPAs) for predicting student performance outcomes. In general, there were small positive correlations with research-only faculty ratings from the first year of medical school, with the highest for social skills/service orientation ( rcorrected = .33, P < .05). Correlations were higher, with the highest for social skills/service orientation and cultural competence ( rcorrected = .33 and .36, respectively, P < .05) in the second year in medical school. SJT scores improved prediction of research-only faculty ratings over MCAT total scores and UGPAs for reliability and dependability/capacity for improvement, cultural competence, social skills/service orientation, and the overall composite score in the first year and for resilience and adaptability, social skills/service orientation, cultural competence, and the overall composite score in the second year. SJT scores demonstrated small correlations with course grades ( rsample-weighted = .10, P = ns) and faculty evaluations related to professionalism skills ( rsample-weighted = .14, P < .05); however, MCAT total scores explained most of the variance associated with course outcomes. These studies provide initial evidence that SJT scores may add value to the medical school admissions process because scores were related to faculty ratings of professional behaviors and provided unique information relative to MCAT scores and UGPAs.

Evaluating a Situational Judgment Test for Use in Medical School Admissions: Two Years of AAMC PREview Exam Administration Data.

To examine the relationship between the Association of American Medical Colleges (AAMC) Professional Readiness Exam (PREview) scores and other admissions data, group differences in mean PREview scores, and whether adding a new assessment tool affected the volume and composition of applicant pools. Data from the 2020 and 2021 PREview exam administrations were analyzed. Two U.S. schools participated in the PREview pilot in 2020 and 6 U.S. schools participated in 2021. PREview scores were paired with data from the American Medical College Application Service (undergraduate grade point averages [GPAs], Medical College Admission Test [MCAT] scores, race, and ethnicity) and participating schools (interview ratings). Data included 19,525 PREview scores from 18,549 unique PREview examinees. Correlations between PREview scores and undergraduate GPAs ( r = .16) and MCAT scores ( r = .29) were small and positive. Correlations between PREview scores and interview ratings were also small and positive, ranging between .09 and .14 after correcting for range restriction. Small group differences in mean PREview scores were observed between White and Black or African American and White and Hispanic, Latino, or of Spanish origin examinees. The addition of the PREview exam did not substantially change the volume or composition of participating schools' applicant pools. Results suggest the PREview exam measures knowledge of competencies that are distinct from those measured by other measures used in medical school admissions. Observed group differences were smaller than group differences observed with traditional academic assessments and evaluations. The addition of the PREview exam did not substantially change the overall volume of applications or the proportions of out-of-state, underrepresented in medicine, or lower socioeconomic status applicants. While more research is needed, these results suggest the PREview exam may provide unique information to the admissions process without adversely affecting applicant pools.

A machine learning approach to graduate admissions and the role of letters of recommendation.

The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants' resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.

COVID-19 and Grade Inflation: Analysis of Undergraduate GPAs During the Pandemic

The COVID-19 pandemic required adaptation to a new learning environment creating challenges for students and instructors. A reduction in student-teacher contact and the lack of supervision should have led to a decline in students’ academic performance. Nonetheless, studies report increases in grades during the pandemic. Yet, limited information is available regarding the persistence of this impact. This study utilizes a hierarchical mixed effect model to estimate the impact of the COVID-19 pandemic on university grades. Using unique class-level data containing chronological variables and institutional, instructor, and student characteristics, spanning Fall 2010 to Spring 2021 of 7,852 undergraduate classes, it is shown class average grade point averages (GPAs) in the College of Agriculture at Texas A&amp;M University increased for the three semesters most impacted by COVID-19. Average class GPAs increased by 0.22 points in Spring 2020 because of COVID-19 and then approximately 0.18 points in the subsequent next two semesters. The negative effect of class size decreased during COVID-19, implying online classes have different size effects than traditional classes. Additionally, the positive effect of SAT scores on grades decreased. One implication of this study is that COVID-19 may not only have a direct, significant, impact on GPAs but may also indirectly affect GPAs through altering the effects of variables on GPAs. The causal mechanisms by which the changes occurred are an area for further research. JEL Codes: I21, I29

Information Science Students’ Background and Data Science Competencies: An Exploratory Study

Many Library and Information Science (LIS) training programs are gradually expanding their curricula to include computational data science courses such as supervised and unsupervised machine learning. These programs focus on developing both “classic” information science competencies as well as core data science competencies among their students. Since data science competencies are often associated with mathematical and computational thinking, departmental officials and prospective students often raise concerns regarding the appropriate background students should have in order to succeed in this newly introduced computational content of the LIS training programs. In order to address these concerns, we report on an exploratory study through which we examined the 2020 and 2021 student classes of Bar-Ilan University's LIS graduate training, focusing on the computational data science courses (i.e., supervised and unsupervised machine learning). Our study shows that contrary to many of the concerns raised, students from the humanities performed as well (and in some cases significantly better) on data science competencies compared to those from the social sciences and had better success in the training program as a whole. In addition, students’ undergraduate GPA acted as an adequate indicator for both their success in the training program and in the data science part thereof. In addition, we find no evidence to support concerns regarding age or sex. Finally, our study suggests that the computational data science part of students’ training is very much aligned with the rest of their training program.

A geographic-location-based medical school admissions process does not influence pre-clerkship and licensing examination academic performance.

Students are selected for admission to the Northern Ontario School of Medicine University (NOSM U) MD degree program using criteria aiming to maximize access of persons thought most likely to practice in the region, including use of a geographic context score (GCS) which ranks those with lived experience in northern Ontario and/or rurality most highly. This study investigates the effect of this admissions process upon medical school academic performance. We used a retrospective cohort design combined with multiple linear regression analysis to investigate the relationship between admission scores and performance on pre-clerkship courses, and the Medical Council of Canada Qualifying Exam Part 1 (MCCQE1).The GCS did not significantly explain performance variance on any pre-clerkship course, nor on the MCCQE1, while the undergraduate Grade Point Average correlated with most assessment scores. The number of prior undergraduate biomedical courses predicted science and clinical skills performance, particularly in Year 1, but not with MCCQE1 scores. Performance on Year 2 courses, particularly foundational sciences and clinical skills, significantly predicted MCCQE1 scores. Our data suggest that admission geographic context scoring is unrelated to future academic performance. Further, students with fewer prior undergraduate biomedical courses may benefit from increased support and/or a modified program during the early years.

Increasing Diversity in the Physician Workforce: Pathway Programs and Predictive Analytics.

Lack of diversity in the physician workforce has well-documented negative impacts on health outcomes. Evidence supports the use of pathway or pipeline programs to recruit underrepresented in medicine students. However, data on how a pathway program should deliver instruction are lacking. This report describes a multiyear project to build such a system with the goal of increasing diversity within medical school cohorts and ultimately the physician workforce. In the 2015-2016 academic year, the Ponce Health Sciences University started a 3-phase project to create a data-driven medical school feeder system by coupling a pathway program with predictive analytics. Phase 1 launched the pathway program. Phase 2 developed and validated a predictive model that estimates United States Medical Licensing Examination (USMLE) Step 1 performance. Phase 3 is underway and focuses on adoption, implementation, and support. Data analysis compared 2 groups of students (pathway vs direct) across specific factors, including Medical College Admission Test (MCAT) score, undergraduate grade point average (GPA), first-generation status, and Step 1 exam performance. Statistically significant differences were found between the 2 groups on the MCAT exam and undergraduate GPA; however, no significant differences were found between groups for first-generation status and performance on the Step 1 exam. This finding supports the authors' hypothesis that although pathway students have significantly lower mean MCAT exam scores compared with direct students, pathway students perform just as well on the USMLE Step 1 exam. Next steps include expanding the project to another campus, adding more socioeconomic status and first-generation data, and identifying best curricular predictors. The authors recommend that medical school programs use pathway programs and predictive analytics to create a more data-centered approach to accepting students with the goal of increasing physician workforce diversity.