Predicting success in graduate entry medical students undertaking a graduate entry medical program (GEM)

Background: Success in undergraduate medical courses in the UK can be predicted by school exit examination (A level) grades. There are no documented predictors of success in UK graduate entry medicine (GEM) courses. This study looks at the examination performance of GEM students to identify factors which may predict success; of particular interest was A level score. Methods: Data was collected for students graduating in 2004, 2005 and 2006, including demographic details (age and gender), details of previous academic achievement (A level total score and prior degree) and examination results at several points during the degree course. Results: Study group comprised 285 students. Statistical analyses identified no significant variables when looking at clinical examinations. Analysis of pass/fail data for written examinations showed no relationship with A level score. However, both percentage data for the final written examination and the analysis of the award of honours showed A level scores of AAB or higher were associated with better performance (p < 0.001). Discussion: A prime objective of introducing GEM programs was to diversify admissions to medical school. In trying to achieve this, medical schools have changed selection criteria. The findings in this study justify this by proving that A level score was not associated with success in either clinical examinations or passing written examinations. Despite this, very high achievements at A level do predict high achievement during medical school. Conclusions: This study shows that selecting graduate medical students with the basic requirement of an upper-second class honours degree is justifiable and does not disadvantage students who may not have achieved high scores in school leaver examinations

The academic value of internships: benefits across disciplines and student backgrounds

While student benefits from internship experience have been frequently documented in research, the emphasis has been on internship effects on employment and career indicators. This work is concerned with effects on academic outcomes and focuses on the robustness of such effects across academic disciplines as well as for different achievement levels of students, student gender, and ethnicity. We present findings from a longitudinal sample (n > 15,000) that covers an extensive range of subjects and disciplines for large Undergraduate cohorts. Main effects and interactions for student background characteristics were investigated showing stable academic benefits for advantaged and disadvantaged students. Further, using ordinal logistic multi-level modelling, we explored the impact on the probability of attaining a higher degree classification for different student scenarios, thus illustrating the practical significance of these internship effects. Effects are less likely to stem from maturation or self-selection. Findings are therefore discussed against a background of motivational approaches suitable to integrate both direct and indirect paths from internship experience to academic outcomes to career indicators

The academic backbone: longitudinal continuities in educational achievement from secondary school and medical school to MRCP(UK) and the specialist register in UK medical students and doctors

Background: Selection of medical students in the UK is still largely based on prior academic achievement, although doubts have been expressed as to whether performance in earlier life is predictive of outcomes later in medical school or post-graduate education. This study analyses data from five longitudinal studies of UK medical students and doctors from the early 1970s until the early 2000s. Two of the studies used the AH5, a group test of general intelligence (that is, intellectual aptitude). Sex and ethnic differences were also analyzed in light of the changing demographics of medical students over the past decades. Methods: Data from five cohort studies were available: the Westminster Study (began clinical studies from 1975 to 1982), the 1980, 1985, and 1990 cohort studies (entered medical school in 1981, 1986, and 1991), and the University College London Medical School (UCLMS) Cohort Study (entered clinical studies in 2005 and 2006). Different studies had different outcome measures, but most had performance on basic medical sciences and clinical examinations at medical school, performance in Membership of the Royal Colleges of Physicians (MRCP(UK)) examinations, and being on the General Medical Council Specialist Register. Results: Correlation matrices and path analyses are presented. There were robust correlations across different years at medical school, and medical school performance also predicted MRCP(UK) performance and being on the GMC Specialist Register. A-levels correlated somewhat less with undergraduate and post-graduate performance, but there was restriction of range in entrants. General Certificate of Secondary Education (GCSE)/O-level results also predicted undergraduate and post-graduate outcomes, but less so than did A-level results, but there may be incremental validity for clinical and post-graduate performance. The AH5 had some significant correlations with outcome, but they were inconsistent. Sex and ethnicity also had predictive effects on measures of educational attainment, undergraduate, and post-graduate performance. Women performed better in assessments but were less likely to be on the Specialist Register. Non-white participants generally underperformed in undergraduate and post-graduate assessments, but were equally likely to be on the Specialist Register. There was a suggestion of smaller ethnicity effects in earlier studies. Conclusions: The existence of the Academic Backbone concept is strongly supported, with attainment at secondary school predicting performance in undergraduate and post-graduate medical assessments, and the effects spanning many years. The Academic Backbone is conceptualized in terms of the development of more sophisticated underlying structures of knowledge ('cognitive capital’ and 'medical capital’). The Academic Backbone provides strong support for using measures of educational attainment, particularly A-levels, in student selection

Predictors of Adolescents’ Interest in Stem Majors and Careers

Advanced Research Winner 2019: The United States currently faces a shortage of qualified workers in fields related to science, technology, engineering, and math (STEM). The first critical step in preventing the labor shortage in STEM is understanding the factors that guide adolescents toward STEM pursuits. Drawing on Eccles’ expectancy-value theory (EVT), the current study aims to identify factors that are relevant to adolescents’ interest in STEM majors and careers. Data were collected from 629 adolescents (Mage = 16.09). Participants attended a high school in northern California and predominantly identified as Asian American (82% of the sample). Preliminary analyses revealed that adolescent boys had higher STEM self-expectancies than did adolescent girls, whereas there was no gender difference in STEM values. Consistent with expectations, multiple regression demonstrated that STEM self-expectancies and values accounted for a significant amount of variance in participants’ interest in STEM majors and careers. STEM value was an especially strong predictor; adolescents tended to be most interested in STEM pursuits when they were also high in STEM value. Moderation analyses showed that the association between STEM value and interest in STEM majors and careers was stronger for girls than for boys. As a whole, this study’s findings suggest that valuing and enjoying STEM pursuits during high school could be an important antecedent of pursuing a STEM major and a STEM career later in life

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Increasing persistence in undergraduate science majors: a model for institutional support of underrepresented students.

The 6-yr degree-completion rate of undergraduate science, technology, engineering, and mathematics (STEM) majors at U.S. colleges and universities is less than 40%. Persistence among women and underrepresented minorities (URMs), including African-American, Latino/a, Native American, and Pacific Islander students, is even more troubling, as these students leave STEM majors at significantly higher rates than their non-URM peers. This study utilizes a matched comparison group design to examine the academic achievement and persistence of students enrolled in the Program for Excellence in Education and Research in the Sciences (PEERS), an academic support program at the University of California, Los Angeles, for first- and second-year science majors from underrepresented backgrounds. Results indicate that PEERS students, on average, earned higher grades in most "gatekeeper" chemistry and math courses, had a higher cumulative grade point average, completed more science courses, and persisted in a science major at significantly higher rates than the comparison group. With its holistic approach focused on academics, counseling, creating a supportive community, and exposure to research, the PEERS program serves as an excellent model for universities interested in and committed to improving persistence of underrepresented science majors and closing the achievement gap

An Empirical Investigation of the Predictors of Executive Career Success

The present study examined the degree to which demographic, human capital,motivational, organizational, and industry/region variables predicted executive career success. Career success was assumed to comprise objective (pay, ascendancy) and subjective (job satisfaction, career satisfaction) elements. Results obtained from a sample of 1,388 U.S.executives suggested that demographic, human capital, motivational, and organizational variables explained significant variance in objective career success and in career satisfaction. Particularly interesting were findings that educational level, quality, prestige, and degree type all predicted financial success. In contrast, only the motivational and organizational variables explained significant amounts of variance in job satisfaction. These findings suggest that the variables that lead to objective career success often are quite different from those that lead to subjectively defined success

Construct-level predictive validity of educational attainment and intellectual aptitude tests in medical student selection: meta-regression of six UK longitudinal studies

Background: Measures used for medical student selection should predict future performance during training. A problem for any selection study is that predictor-outcome correlations are known only in those who have been selected, whereas selectors need to know how measures would predict in the entire pool of applicants. That problem of interpretation can be solved by calculating construct-level predictive validity, an estimate of true predictor-outcome correlation across the range of applicant abilities. Methods: Construct-level predictive validities were calculated in six cohort studies of medical student selection and training (student entry, 1972 to 2009) for a range of predictors, including A-levels, General Certificates of Secondary Education (GCSEs)/O-levels, and aptitude tests (AH5 and UK Clinical Aptitude Test (UKCAT)). Outcomes included undergraduate basic medical science and finals assessments, as well as postgraduate measures of Membership of the Royal Colleges of Physicians of the United Kingdom (MRCP(UK)) performance and entry in the Specialist Register. Construct-level predictive validity was calculated with the method of Hunter, Schmidt and Le (2006), adapted to correct for right-censorship of examination results due to grade inflation. Results: Meta-regression analyzed 57 separate predictor-outcome correlations (POCs) and construct-level predictive validities (CLPVs). Mean CLPVs are substantially higher (.450) than mean POCs (.171). Mean CLPVs for first-year examinations, were high for A-levels (.809; CI: .501 to .935), and lower for GCSEs/O-levels (.332; CI: .024 to .583) and UKCAT (mean = .245; CI: .207 to .276). A-levels had higher CLPVs for all undergraduate and postgraduate assessments than did GCSEs/O-levels and intellectual aptitude tests. CLPVs of educational attainment measures decline somewhat during training, but continue to predict postgraduate performance. Intellectual aptitude tests have lower CLPVs than A-levels or GCSEs/O-levels. Conclusions: Educational attainment has strong CLPVs for undergraduate and postgraduate performance, accounting for perhaps 65% of true variance in first year performance. Such CLPVs justify the use of educational attainment measure in selection, but also raise a key theoretical question concerning the remaining 35% of variance (and measurement error, range restriction and right-censorship have been taken into account). Just as in astrophysics, ‘dark matter’ and ‘dark energy’ are posited to balance various theoretical equations, so medical student selection must also have its ‘dark variance’, whose nature is not yet properly characterized, but explains a third of the variation in performance during training. Some variance probably relates to factors which are unpredictable at selection, such as illness or other life events, but some is probably also associated with factors such as personality, motivation or study skills

Tertiary Performance, Field of Study and Graduate Starting Salaries

This paper analyses data from the University of Western Australia (UWA) Graduate Destination Survey linked to information from the University’s Student Records System to explore the determinants of graduates’ starting salaries over the years 2002 to 2004. While the details examined also include age, gender, language spoken at home, country of birth, disability status and high school attended, most emphasis is placed on the impact on starting salaries of students’ academic performance and their field of study. The analyses show that the main determinant of graduates’ starting salaries is the weighted average mark they achieve at university. The salary differentials associated with higher marks in the Australian labour market appear greater than those reported in similar studies of the United States and United Kingdom labour markets. Science graduates are shown to have relatively low starting salaries, casting a shadow over recent suggestions that the supply of this group be increased through lower fee regimes.Starting Salaries, Ability, Field of Study