USMLE performances in a predominantly Asian and Pacific Islander population of medical students in a problem-based learning curriculum

Predictors of academic success for Māori, Pacific and non-Māori non-Pacific students in health professional education: a quantitative analysis

Tertiary institutions internationally aim to increase student diversity, however are struggling to achieve equitable academic outcomes for indigenous and ethnic minority students and detailed exploration of factors that impact on success is required. This study explored the predictive effect of admission variables on academic outcomes for health professional students by ethnic grouping. Kaupapa Māori and Pacific research methodologies were used to conduct a quantitative analysis using data for 2686 health professional students [150 Māori, 257 Pacific, 2279, non-Māori non-Pacific (nMnP)]. The predictive effect of admission variables: school decile; attending school in Auckland; type of admission; bridging programme; and first-year bachelor results on academic outcomes: year 2-4 grade point average (GPA); graduating; graduating in the minimum time; and optimal completion for the three ethnic groupings and the full cohort was explored using multiple regression analyses. After adjusting for admission variables, for every point increase in first year bachelor GPA: year 2-4 GPA increased by an average of 0.46 points for Māori (p = 0.0002, 95% CI 0.22, 0.69), 0.70 points for Pacific (p < 0.0001, CI 0.52, 0.87), and 0.55 points for nMnP (p < 0.0001, CI 0.51, 0.58) students. For the total cohort, ethnic grouping was consistently the most significant predictor of academic outcomes. This study demonstrated clear differences in academic outcomes between both Māori and Pacific students when compared to nMnP students. Some (but not all) of the disparities between ethnic groupings could be explained by controlling for admission variables.

The social and learning environments experienced by underrepresented minority medical students: a narrative review

PURPOSE

To review the literature on the social and learning environments experienced by underrepresented minority (URM) medical students to determine what type of interventions are needed to eliminate potential barriers to enrolling and retaining URM students.

METHOD

The authors searched MEDLINE, PubMed, Ovid HealthStar, and Web of Science, and the reference lists of included studies, published between January 1, 1980, and September 15, 2012. Studies of the learning and social environments and of students' satisfaction, experiences with discrimination or unfair practices, and academic performance or progress, as well as assessments of programs or interventions to improve URM students' academic performance, were eligible for inclusion.

RESULTS

The authors identified 28 studies (27 unique data sets) meeting the inclusion criteria. The results of the included studies indicated that URM students experienced less supportive social and less positive learning environments, were subjected to discrimination and racial harassment, and were more likely to see their race as having a negative impact on their medical school experiences than non-URM students. Academic performance on standardized exams was worse, progress less timely, and attrition higher for URM students as well.

CONCLUSIONS

For URM students, an adverse climate may be decreasing the attractiveness of careers in medicine, impairing their academic performance, and increasing attrition. Improvements to the social and learning environments experienced by URM students are needed to make medicine a more inclusive profession. The current environment of health care reform creates an opportunity for institutions to implement strategies to achieve this goal.

Evaluation and treatment of students with difficulties passing the Step examinations

PURPOSE

The author designed this retrospective case series study both to systematically examine characteristics of individuals referred for treatment after multiple failures on the United States Medical Licensing Examinations (USMLE) Step 1 or 2 administered by the National Board of Medical Examiners and to evaluate treatment effectiveness in a uniform sample.

METHOD

Six medical students referred to rehabilitation psychology met selection criteria. All students completed the requisite neuropsychological, academic, and psychological testing to identify cognitive and emotional strengths and weaknesses. All six underwent individualized cognitive rehabilitation (CR) with a primary focus on reading fluency and accuracy.

RESULTS

All participants improved on a quantitative measure of reading speed and accuracy, and five of the six passed their next USLME Step examination in spite of past failures.

CONCLUSIONS

Medical students with identified difficulties on reading fluency, but no history of a learning disability, may benefit from systematic CR that addresses cognitive weaknesses related to test-taking abilities. The strong relationships between language and reading skills and the USMLE Step examinations suggest that some students may fail these examinations because of a relative weakness in language processing and reading fluency that may prohibit their successful completion of the Step examinations.

Impact of preadmission variables on USMLE step 1 and step 2 performance

PURPOSE

To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model.

METHOD

Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of performance on step 1 and step 2. A generalized regression neural network (GRNN) as well as a feed forward neural network (FFNN) was then developed in an effort to more accurately predict step 1 and step 2 scores from these preadmission data.

RESULTS

Statistically significant predictors for step 1 and step 2 included science grade point average (SGPA), the biologic science (BS) section of the Medical College Admissions Test (MCAT), college selectivity, race, and age of the applicant. Neural networks were found to predict a significant portion of the variance, and the FFNN demonstrated some superiority over that obtained with linear regression models as well as the GRNN.

CONCLUSIONS

The results have implications that could impact the selection of applicants to medical school and the neural networks that we developed could be used in a prospective manner.

Logistic Regression Analysis to Predict Medical Licensing Examination of Thailand (MLET) Step1 Success or Failure

Introduction: The aim of this paper was to assess factors that predict students’ performance in the Medical Licensing Examination of Thailand (MLET) Step1 examination. The hypothesis was that demographic factors and academic records would predict the students’ performance in the Step1 Licensing Examination. Materials and Methods: A logistic regression analysis of demographic factors (age, sex and residence) and academic records [high school grade point average (GPA), National University Entrance Examination Score and GPAs of the pre-clinical years] with the MLET Step1 outcome was accomplished using the data of 117 third-year Ramathibodi medical students. Results: Twenty-three (19.7%) students failed the MLET Step1 examination. Stepwise logistic regression analysis showed that the significant predictors of MLET Step1 success/failure were residence background and GPAs of the second and third preclinical years. For students whose sophomore and third-year GPAs increased by an average of 1 point, the odds of passing the MLET Step1 examination increased by a factor of 16.3 and 12.8 respectively. The minimum GPAs for students from urban and rural backgrounds to pass the examination were estimated from the equation (2.35 vs 2.65 from 4.00 scale). Conclusions: Students from rural backgrounds and/or low-grade point averages in their second and third preclinical years of medical school are at risk of failing the MLET Step1 examination. They should be given intensive tutorials during the second and third pre-clinical years. Key words: Basic science, National test, Prediction

The predictive validity of the MCAT for medical school performance and medical board licensing examinations: a meta-analysis of the published research

PURPOSE

To conduct a meta-analysis of published studies to determine the predictive validity of the MCAT on medical school performance and medical board licensing examinations.

METHOD

The authors included all peer-reviewed published studies reporting empirical data on the relationship between MCAT scores and medical school performance or medical board licensing exam measures. Moderator variables, participant characteristics, and medical school performance/medical board licensing exam measures were extracted and reviewed separately by three reviewers using a standardized protocol.

RESULTS

Medical school performance measures from 11 studies and medical board licensing examinations from 18 studies, for a total of 23 studies, were selected. A random-effects model meta-analysis of weighted effects sizes (r) resulted in (1) a predictive validity coefficient for the MCAT in the preclinical years of r = 0.39 (95% confidence interval [CI], 0.21-0.54) and on the USMLE Step 1 of r = 0.60 (95% CI, 0.50-0.67); and (2) the biological sciences subtest as the best predictor of medical school performance in the preclinical years (r = 0.32 95% CI, 0.21-0.42) and on the USMLE Step 1 (r = 0.48 95% CI, 0.41-0.54).

CONCLUSIONS

The predictive validity of the MCAT ranges from small to medium for both medical school performance and medical board licensing exam measures. The medical profession is challenged to develop screening and selection criteria with improved validity that can supplement the MCAT as an important criterion for admission to medical schools.