Differences in doctors’ career progression and attainment by medical degree course type

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However, evidence indicates attainment gaps for graduates from different course types. It is essential to determine whether any cohort faces systematic disadvantage during training. This paper examines the postgraduate (PG) performance of graduates of the three main UK undergraduate medical degree course types. Methods This was a retrospective study using the UK Medical Education Database (UKMED). Data were included from doctors who graduated between 2012 and 2022, were UK domiciled, with at least one PG outcome: Annual Review of Competency Progression (ARCP) outcomes (assessment within the training workplace), performance on first attempt at a UK Royal College examination, and time taken to sit the examination. Multivariate logistic regression analyses evaluated PG attainment for graduates from Standard, Graduate or Gateway Programs, while adjusting for prior attainment and demographic factors including ethnicity, area measures of disadvantage and first-in-family to attend university. Results Data were available for 41,290 standard entry (SEM), 5,055 graduate entry (GEM), and 770 gateway (GY) graduates. Significant associations were found between course type and all sociodemographic variables except for sex. SEM graduates were more likely to pass their first UK Royal College exam than GEM or GY graduates (ORs 0.86 and 0.37 respectively, p < 0.001 for both comparisons). Adjusting for all sociodemographic variables increased the OR to 0.49 for GY graduates and returned it to 1.05 for GEM graduates. The median time to sit the first Royal College exam was longer for GY graduates (41 months compared to 29 for SEM and 30 for GEM, p < 0.001). There were no differences in ARCP outcomes. Conclusions There are significant differences in postgraduate progression for UK Royal College exam performance and time taken to sit exams associated with undergraduate course type, particularly GY courses. These differences remain after accounting for prior attainment and socioeconomic factors, so may be associated with the training environment. Postgraduate learning providers, and those who regulate postgraduate examinations and training must now examine what underpins these differences, to ensure equitable opportunities to progress for all medical graduates. Attainment gap Postgraduate medical education Widening participation in medicine Figures Figure 1 Figure 2 Figure 3 Introduction The attainment or awarding gap, also known as differential attainment, refers to disparities in achievement between groups that can arise from differences in educational opportunities, demographic and socioeconomic factors [ 1 , 2 , 3 , 4 ]. Evidence from across the globe shows that the awarding gap exists across all stages of medical education and training, spanning multiple specialties and assessment types - including written and clinical exams, and specialty training selection. These gaps have been reported in relation to ethnicity, age, sex, and disability/neurodiversity in many different contexts [ 2 , 3 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. There is increasing interest in looking beyond sociodemographic data to also examine whether there are structural and systemic factors within medical education and training that impact on relative performance. For example, there is emerging evidence that there are differences in attainment associated with the type of primary medical course. In the UK this would include: Standard entry to medicine courses (SEM), 5- or 6-year programs courses for students who usually enter directly from school/college; 4-year Graduate entry courses (GEM), for students who already have a university level degree; and Gateway courses (GY), which have an additional year of study at the start of the course for students from educationally or socioeconomically disadvantaged backgrounds. Comparable frameworks exist internationally: there are GEM courses in Australia and GY/pipeline programs in the United States [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Evidence shows that medical schools provide a positive learning environment that can level out prior educational advantages or disadvantages [ 19 ]. The entry requirements for GY programs are lower than those for SEM and GEM courses because applicant achievements are considered against their educational and social circumstances [ 18 , 20 ]. However, while the gap between GY and SEM students narrows during medical school, with the differences on entry greater than the differences on exit [ 21 ], longitudinal research indicates that the gap between those graduating from GY and SEM courses increases again in postgraduate training [ 22 ]. This may suggest that GY graduates are disadvantaged later in the postgraduate (PG) medical training pathway, potentially at key progression points such as Royal College examinations (a prerequisite for entry into higher specialty training). Interestingly, although approximately 8.5% of 2021 UK medical school students entered GEM programs [ 23 ], to the best of our knowledge, there has been no direct comparison of the relative progression of GEM, SEM and GY students after graduation. This is an important gap to address: there are conflicting results from comparisons of PG progression for GEM vs SEM students, with some authors reporting delays in passing exams [ 17 ], but others reporting no difference [ 12 ] or higher attainment in workplace-based assessments [ 22 ]. However, much of this previous work, particularly that with GEM graduates, does not widely control for sociodemographic factors, which are known to be associated with attainment [ 2 , 3 , 5 , 6 , 7 , 10 ]. These differences and gaps in outcomes are problematic. Enabling equity and fairness and supporting all doctors in achieving their full potential within medical education and training depends on identifying and addressing barriers to progression [ 24 , 25 , 26 , 27 , 28 ]. It is thus critical to identify whether there are systemic disadvantages associated with different course types. This issue has recently come to the attention of policy makers. For example, in the UK, the Medical Schools Council report ‘Fostering Potential’ (2024) emphasises the importance of reducing barriers to a medical career for students from disadvantaged backgrounds from “admission through medical school into postgraduate training” [ 20 ]. Our aim in this paper is to explore the relationships among course type, career progression and attainment in UK Royal College exams, controlling for demographic differences between cohorts. Methods Study context In the UK, medical graduates, known as resident doctors, undergo foundation training, a two-year, work-based training scheme that bridges medical school and specialty training, ensuring that newly qualified doctors develop clinical and professional skills for subsequent training stages [ 29 ]. Doctors may then progress to specialty training before being awarded a certificate of completion of PG training. Progression through this PG journey is dependent upon passing UK Royal College exams and completing satisfactory progress assessments known as the Annual Record of Competency Progression (ARCPs; detailed below). Graduates from all UK course types, GY, GEM and SEM, enter the same PG learning environment. Study population This was a retrospective study using data from the UK Medical Education Database (UKMED). The UKMED links undergraduate (UG) and PG educational outcome data and progression data for medical students and resident doctors. UKMED classifies students according to the course type into which they are enrolled. This means that although students on a gateway programme (GY) usually undertake the same curriculum as SEM students do after the additional year at the start of the programme, they can be identified by their GY classification. Data were provided for UK-domiciled entrants who graduated from a SEM, GEM or GY course at a UK medical school between 2012 and 2022. This timeframe allows for sufficient postgraduate follow-up to capture meaningful progression data, and the dataset includes those who started earlier and may have completed specialty training and those who are still progressing through training. All included graduates had at least one postgraduate outcome. PG outcomes Several key outcome measures were selected to assess differences in PG progression and attainment. These included: Foundation Programme and specialty training ARCP Outcomes . The ARCP is an annual review of each doctor in PG training to ensure that they are offering safe, high-quality patient care and are adequately progressing through their training program. A standard ARCP outcome is needed to progress onto the subsequent year of training, including progression from Foundation programme (spanning the first two years postgraduation) to specialty training (following successful completion of the Foundation Programme, where doctors enter a focused training programme in a chosen medical specialty). Although ARCPs have a role in providing feedback to the individual doctor as well as a summative function, for this study outcomes were dichotomised into ‘standard’ or ‘non-standard’, as previously described [ 29 ]. Time taken to sit first UK Royal College exam. The number of months between graduation and the sitting of each resident doctor’s first exam were calculated. Timely completion of these exams, awarded by the UK Royal Colleges, is necessary to avoid delaying progression. Passing them provides an advantage in obtaining a specialty training program post and is essential to completing higher specialty training. Pass rate at first attempt at a UK Royal College exam. The names, details and timings of UK Royal College exams differ greatly by specialty. Several exams had a limited number of cases for GY and GEM graduates, therefore the consolidated variable ‘pass on first attempt at any UK Royal College exam’ was used, which included each individual’s first ever recorded attempt at a PG exam. All exams included had at least 5 cases per course type and are listed in the Additional File: Table S1 . Pass rates were then derived for each course type. Sociodemographic variables We controlled for sociodemographic differences previously established as relevant in comparisons of GY and SEM undergraduates and postgraduates and more widely in medical education [ 10 , 21 , 22 , 31 ]. The following variables were included: Sex. Gender data were not available, as UKMED currently records only sex, with options limited to male and female. Ethnicity. Ethnicity was included at a ‘high level’ and was grouped as Asian/Asian British, Black/Black British, Mixed, Other, and White. This was chosen over dichotomising into Minority Ethnic and White, due to the large variations in the ethnic backgrounds for each course type. For instance, 21% of GY graduates were Black, compared to less than 3% of GEM or SEM graduates. Age at graduation. Age at graduation was grouped into < 29 or ≥ 29, as this cut-off has been used by previous authors [ 6 , 10 ]. Graduate on entry. Indication as to whether a previous degree had been completed prior to medical school, or not. This variable is essential as applicants with prior degrees can also enter SEM courses. There is a strong correlation between age and being a graduate on entry. Index of Multiple Deprivation (IMD) group and Participation of Local Area (POLAR) group. IMD and POLAR are postcode-based measures which assess relative socioeconomic disadvantage and participation into higher education, respectively. Both were set as missing for all individuals who entered medical school as a graduate, as the address given on application was likely not their parental home. First in family to attend university . Indication of whether either parent holds a degree, or not. Secondary school type. Grouped by whether state- or private-funded education. Prior attainment The medical school performance component of the Educational Performance Measure (EPM) was used as a measure of prior attainment. The EPM was a national measure used by the UK Foundation Programme Office (UKFPO) in combination with a Situational Judgement Test to rank graduates for the Foundation Programme allocation between 2012 and 2022. EPM has been widely used as a measure of prior attainment in other studies examining progression through medical education and training [ 12 , 21 , 22 , 30 , 31 ]. EPM was available for > 98% of graduates in the dataset. From 2013, all graduates were ranked within their year and medical school into EPM deciles, whereas in 2012, applicants were ranked into quartiles. To allow cases from 2012 and the later years to be combined in one analysis, EPM quartiles and deciles were converted to normal deviate scores (as per Garrud and McManus [ 12 ]). Missing data Missing data were imputed by chained equations using the mice package in R. Predictive mean matching was employed as the imputation method, with all other variables used as predictors. Multiple imputations were generated, and the most plausible model was selected. Statistical analysis All analyses were conducted in R Studio (version 4.4.0) within the University of Dundee’s HIC TRE Safe Haven. χ2 tests were used to assess the relationships between course type and PG measures. Any significant associations (after Bonferroni correction for multiple testing) were taken through into a multivariate analysis. A Spearman's rank correlation coefficient matrix was constructed to assess any monotonic relationships between variables (Additional File: Figure S1 ), and a cut-off threshold of 0.4 was used, such that if it was exceeded, then only one variable from each pair would be entered into the model. Logistic regression models were created to predict outcomes by course type, with further models controlling for demographics and EPM. The goodness of fit of each model was subsequently assessed using the Hosmer-Lemeshow test (p > 0.05) and significance of independent variables was assessed using a χ2 test (p < 0.05). Cohen’s d was calculated to measure the effect size of EPM decile scores between course types. A time-to-event graph (inverse Kaplan-Meier) was used to plot the time in months between starting foundation training and first sitting of a UK Royal College examination for each course type. Patient and public involvement No patients or members of the public were involved in this study. Ethics The project was approved by the University Research Ethics Committee at the University of Southampton (application no. 92852). No patients or members of the public were involved in the study. Results Demographic and educational performance variations by course type The demographic data for graduates by course type are presented in Table 2 . Significant associations ( p < 0.001) were found between course type and all sociodemographic variables, except for sex ( p = 0.05). GY graduates were more likely to be from a minoritised ethnic background and disadvantaged socioeconomic background. A greater proportion of GEM graduates than to SEM and GY graduates were White or aged 29 years or older. Table 1. Postgraduate outcome data availability by course type. PG outcome GEM GY SEM UK-domiciled graduate 5475 920 48190 Annual Review of Competency Progression (ARCP) 5045 765 41190 UK Royal College exam 3785 510 29530 UK-domiciled graduate with 1+ PG outcomes 5055 770 41290 Table 2 Demographics and missing data by course type. Demographic Course Type p value GEM GY SEM N % N % N % Sex Male 2310 45.7 345 44.9 18130 43.9 0.05 Female 2745 54.3 425 55.3 23160 56.1 Ethnicity Asian or Asian British 490 9.7 270 35.1 8770 21.2 < 0.001 Black or Black British 145 2.9 165 21.5 1050 2.5 Mixed 235 4.6 30 3.9 1680 4.1 Other 65 1.3 35 4.6 1005 2.4 Other 65 1.3 35 4.6 1005 2.4 White 3905 77.2 230 29.9 27675 67 Missing 220 4.3 40 5.2 1110 2.7 Age at graduation < 29 3060 60.5 715 93 39130 94.8 =29 1995 39.4 50 6.5 2160 5.2 IMD* Q3-Q5 0 0 300 39 30815 74.6 < 0.001 Q1-Q2 0 0 465 60.5 6255 15.1 Missing 5060 100 0 0 4220 10.2 POLAR* Q3-Q5 0 0 565 73.5 32630 79 < 0.001 Q1-Q2 0 0 205 26.7 4425 10.7 Missing 5060 100 0 0 4235 10.3 Parent holds a degree No 1140 22.5 425 55.3 7535 18.3 < 0.001 Yes 3075 60.8 180 23.4 27210 65.9 Missing 845 16.7 160 20.8 6545 15.9 School Type Private 310 6.1 20 2.6 11505 27.9 < 0.001 State 3430 67.8 745 96.9 28190 68.3 Missing 1320 26.1 10 1.3 1600 3.9 P-values were obtained from χ2 tests with Bonferroni correction for multiple testing. *IMD and POLAR set to missing for all graduate-on entry graduates. There were significant differences when comparing the EPM deciles by course type. EPM decile distributions were plotted (Fig. 1 ), which show an even distribution for SEM, but a left-skewed distribution for GEM and a right-skewed distribution for GY. This indicates that GY graduates are ranked lower overall than their peers upon completion of medical school, whereas GEM graduates are ranked higher. A comparison of the groups revealed a large effect size when comparing between both GEM and SEM (0.81 95% CI 0.84 to 0.78) and GY (1.93, 95% CI 1.84 to 2.02) courses, and a small effect size between SEM and GY (0.28, 95% CI 0.20 to 0.35). [Figure 1 here] Univariate analysis The results of the univariate analysis between course type and each outcome measure are shown in Table 3 . A lower proportion of GY graduates passed their first attempt at a UK Royal College exam compared to both SEM and GEM graduates, who had comparable rates. In contrast, the percentage of resident doctors awarded a successful ARCP outcome during Foundation or Specialty Training did not differ by course type (p = 1). In contrast, the percentage of resident doctors being awarded a successful ARCP outcome during Foundation or Specialty Training did not differ by course type (p = 1). Table 3 Univariate analysis of PG outcomes by course type. Postgraduate outcome GEM GY SEM p value N % (95% CI) N % (95% CI) N % (95% CI) Passed first attempt at a UK Royal College exam 3785 70.8 (69.4–72.3) 510 50.6 (46.3–54.9) 29530 73.7 (73.2–74.2) < 0.001 Successful First Foundation ARCP 5040 99.5 (99.3–99.7) 765 99.3 (98.8–99.9) 41175 99.6 (99.5–99.7 1 Successful First Specialty ARCP 3010 98.2 (97.7–98.7) 360 97.5 (95.9–99.1 20770 98.3 (98.1–98.5) 1 Ns represent the total number of available cases per course type. P values were calculated using χ2 tests and adjusted with Bonferroni correction. Multivariate analysis There were significant variations by course type in terms of the proportions of candidates passing a UK Royal College exam on first sitting, so this was included in the multivariate logistic regression analysis (Table 4 ). Initial univariate regression analyses (Model 1) evaluated course type as a predictor for each measure, while multivariate regression analyses further adjusted for ethnicity (Model 2), all sociodemographic factors (Model 3) and all sociodemographic factors and prior attainment (Model 4). The univariate analysis revealed that compared with SEM, GY and GEM graduates had significantly lower odds of passing their UK Royal College exam on first sitting (OR = 0.37 and 0.86, respectively; p < 0.001 for both). For GEM, the association remained significant after adjusting for ethnicity, but after adjusting for all demographic variables the association became non-significant (Odds Ratio (OR) = 1.05, p = 0.23), suggesting that these variables may account for the observed differences. Interestingly, when the model was further adjusted to include EPM, the association became statistically significant once more (OR = 0.89, p < 0.05). For GY, the association remained significant after each model adjustment (Models 2–4), with the OR gradually increasing across the models. This suggests that while demographic factors and prior attainment explain some of the observed disadvantages for GY graduates, additional unmeasured or contextual factors may also contribute to the observed lower odds ratio. A forest plot (Fig. 2 ) illustrates the significant difference seen in Model 4 for GEM and GY relative to the SEM course type. Table 4 Multivariate Logistic Regression of UK Royal College exam First Attempt Pass Rates by Course Type. Outcome measure Course Type Model 1 – Univariate Model 2 – adjusted for Ethnicity Model 3 – adjusted for all demographics Model 4 – adjusted for demographics and EPM OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value OR (95% CI) p value Passed first attempt of a UK Royal College exam SEM Reference Reference Reference Reference GEM 0.86 (0.80 to 0.92) < 0.001 0.85 (0.80 to 0.90) < 0.001 1.05 (0.97 to 1.12) 0.228 0.89 (0.83 to 0.97) < 0.05 GY 0.37 (0.33 to 0.46) < 0.001 0.45 (0.39 to 0.55) < 0.001 0.49 (0.41 to 0.58) < 0.001 0.57 (0.48 to 0.68) < 0.001 OR = Odds Ratio; 95% CI = 95% Confidence Interval. [Figure 2 here] Time taken for the first attempt at a UK Royal College exam A significant delay from commencing work as a doctor to sitting the first UK Royal College exam can be seen for GY graduates compared with SEM and GEM graduates (Fig. 3 ). Prior to completion of the Foundation Training Programme (time ≈ 24 months), 39.0% and 38.3% of SEM and GEM graduates, respectively, had sat their first medical PG exam, compared to 27.4% of GY graduates. It took an additional year for half of the GY graduate cohort to sit their first medical PG exam (time = 41 months, p < 0.001), compared with SEM (time = 29 months) and GEM (time = 30 months). [Figure 3 here] Discussion This study is the first to explore postgraduate (PG) attainment by course type. Our findings strongly suggest that, in the UK, graduates from Gateway (GY) courses experience greater challenges with medical PG examinations in comparison to standard entry medicine (SEM) graduates, and that these challenges are not purely a result of differences in either ethnicity, previous academic attainment or socioeconomic background. Graduates from graduate entry (GEM) courses also experience challenges, although the magnitude is smaller. Specifically, we found differences between SEM, GEM and GY graduates with respect to pass rates on the first sitting of a UK Royal College exam and the time from graduation to sitting these exams. Graduates of GEM and GY courses were significantly less likely to pass PG exams at the first attempt and GY graduates took a median of 11 months longer before attempting a UK Royal College exam for the first time. These differences were found despite controlling for sociodemographic variables, prior academic achievement and ethnicity. In contrast, there was no difference between groups in terms of performance on ARCPs, suggesting that workplace-based assessments do not contribute to the attainment gap. We have also provided evidence that GY courses are an effective method for broadening the demographics of the medical workforce. GY graduates are more likely to be from a minority ethnic group, from less affluent backgrounds, less likely to have a parent with a degree, and less likely to have attended a fee-paying school. GEM graduates were more likely to be from a white background and be aged 29 years or older. This aligns with previous studies comparing GY and SEM students and graduates [ 21 , 22 ] but this is the first time the demographics of graduates from the three different types of courses have been compared directly. The causes for variable journeys through the same PG learning environment are unknown but are likely to be complex and multifactorial. We propose three possible and potentially overlapping causes: the medical school course itself; the candidates and their life outside of work; and the PG learning environment including the workplace itself, teaching by colleagues and the UK Royal College examinations. We have shown that the candidates differ between course types in terms of demographics. These differences may be associated with different challenges and commitments outside work; for example, in terms of financial and family commitments. In turn, different commitments may impact on candidates’ ability to accommodate the financial and time commitments involved in preparing for and sitting PG exams [ 32 ]. We know that implicit bias within the learning environment is a cause of other attainment gaps in PG medicine [ 33 , 34 ]. Although our data cannot confirm this intersectionality, social categorisations such as race, class and sex are interconnected, leading to overlapping and complex patterns of disadvantage, which may be a contributing factor to our findings [ 34 , 35 , 36 ]. This study has identified differences in progression related to course type. We have shown that these challenges are not purely a result of differences in ethnicity, previous academic attainment or socioeconomic background. Further research is urgently needed to understand the challenges GEM and GY doctors experience with sitting and passing UK Royal College exams, and to seek to explain the attainment gap seen in exams but not in workplace-based assessments. Postgraduate learning providers and the Royal Colleges need this information so that they can work towards providing an equitable training experiences for graduates of all course types. Moreover, we urge those working in other contexts with different types of medical courses running in parallel (e.g., Australia with SEM and GEM courses, the USA with GY and SEM courses) to carry out similar analyses to identify whether differences in progression related to course type are a widespread issue. The strengths of this study are that it is a large cohort (N = 47,115), is country-wide, and covers multiple graduating cohorts of doctors. Individual demographics were controlled for, and the study used relevant and objective outcome measures. Limitations include that this study is purely observational, and confounding factors such as the use of EPM as a measure of prior attainment could also be challenged as institutional approaches to determining the EPM scores differ slightly. For example, some institutions applied a single EPM across all course types, whereas others applied the EPM by course type. Additionally, only the course performance ranking component of the EPM was used, because of large variations in additional degrees and publications by course type and their associated financial and time commitments, which could introduce inequity, particularly for GY graduates. In conclusion, establishing different course types, especially gateway courses, has increased socioeconomic diversity in medical student and resident doctor populations. Different course types have introduced an attainment gap on entry to medical school which although is seen to reduce on exit, widens again during postgraduate training. In this study we have provided the necessary evidence to show that there is an issue. These findings must be used to help inform improvements in postgraduate training programs and their working environments to enable a fair and equitable experience for all resident doctors. Abbreviations ARCP Annual Review of Competency Progression EPM Educational Performance Measure GEM Graduate Entry Medicine GY Medicine with a Gateway Year IMD Index of Multiple Deprivation OR Odds ratio PG Postgraduate POLAR Participation of Local Area SEM Standard Entry Medicine UG Undergraduate UKFPO UK Foundation Programme Office UKMED UK Medical Education Database Declarations Ethics approval and consent to participate The project was approved by the University Research Ethics Committee at the University of Southampton (application no. 92852). No patients or members of the public were involved in the study. Consent for publication Not applicable. Availability of data and materials The data used in this study were accessed through the HiC Dundee Safe Haven under the UKMED application process. Access was granted following approval of our application and signing of a Data Sharing Agreement (DSA). Data were provided in pseudonymised form and were stored in a secure environment; therefore, they cannot be shared publicly. Researchers who meet the criteria for access can request access via the UKMED application process and use of the HiC Dundee Safe Haven. The source of the data used in this project is the UK Medical Education Database (UKMED) [project number ‘P194’], with the extract generated on 24/05/2024. Approved for publication on 6th June 2025. I am grateful to UKMED for the use of these data. However, UKMED bears no responsibility for their analysis or interpretation. The data includes information derived from that collected by the Higher Education Statistics Agency Limited (“HESA”) and provided to the GMC (“HESA Data”), source: HESA Student Record 2006 to 2018. Copyright Higher Education Statistics Agency Limited. The Higher Education Statistics Agency Limited makes no warranty as to the accuracy of the HESA Data and cannot accept responsibility for any inferences or conclusions derived by third parties from data or other information supplied by it. Competing interests All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest. JCl is the Editor in Chief of Medical Teacher. All authors declare no other financial relationships with organisations that might have an interest in the submitted work in the previous three years and no other relationships or activities that could appear to have influenced the submitted work. Funding No external funding was used. Authors' contributions EF is the guarantor of the study. EF, SC and JCa conceived the study. EF performed the statistical analysis. EF, SC, JCa and MM interpreted the data. EF, SC, JCa, MM and JCl drafted and critically revised the manuscript. All authors approved the final version. Acknowledgements Not applicable. References Mountford-Zimdars A, Sabri D, Moore J, Sanders J, Jones S, Higham L. Causes of differences in student outcomes (HEFCE). London: HEFCE; 2015. Wikaire E, Curtis E, Cormack D, et al. Predictors of academic success for Māori, Pacific and non-Māori non-Pacific students in health professional education: a quantitative analysis. Adv Health Sci Educ Theory Pract. 2017;22(2):299–326. Teherani A, Hauer KE, Fernandez A, King TE, Lucey C. How small differences in assessed clinical performance amplify to large differences in grades and awards: a cascade with serious consequences for students underrepresented in medicine. Acad Med. 2018;93(8):1286–92. General Medical Council. 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The relationship between school type and academic performance at medical school: a national, multi-cohort study. BMJ Open. 2017;7:e016291. 10.1136/bmjopen-2017-016291 . Medical Schools Council. Fostering potential: 10 years on from Selecting for Excellence [Internet]. London: Medical Schools Council. 2024 [cited 2025 Sep 21]. Available from: https://www.medschools.ac.uk/latest/publications/selecting-for-excellence-final-report/ Curtis S, Smith D. A comparison of undergraduate outcomes for students from gateway courses and standard entry medicine courses. BMC Med Educ. 2020;20:4. 10.1186/s12909-019-1918-y . Elmansouri A, Curtis S, Nursaw C, Smith D. How do the post-graduation outcomes of students from gateway courses compare to those from standard entry medicine courses at the same medical schools? BMC Med Educ. 2023;23:298. 10.1186/s12909-023-04179-3 . General Medical Council. Entrants. GMC Education Data Tool: Progression Reports [Internet]. London: GMC. 2025 [cited 2025 Nov 10]. Available from: https://edt.gmc-uk.org/progression-reports/entrants Curtis S, Blundell C, Platz C, Turner L. Successfully widening access to medicine. Part 2: curriculum design and student progression. J R Soc Med. 2014;107(10):393–7. 10.1177/0141076814538787 . Krstić C, Krstić L, Tulloch A, Agius S, Warren A, Doody GA. The experience of widening participation students in undergraduate medical education in the UK: a qualitative systematic review. Med Teach. 2021;43(9):1044–53. 10.1080/0142159X.2021.1908976 . Venkataraman S, Nguyen M, Boatright D. Barriers to advancement—unequal opportunities in academic promotion based on race, ethnicity, and gender. JAMA Netw Open. 2024;7(11):e2445971. 10.1001/jamanetworkopen.2024.45971 . Brown MEL, Burford B, Vance G. Barriers, facilitators and solutions to equitable career progression for disabled doctors: an integrative review. BMJ Open. 2025;15:e106639. 10.1136/bmjopen-2025-106639 . UK Foundation Programme Office. Introduction to the UK Foundation Programme [Internet]. London: UK Foundation Programme Office. 2025 [cited 2025 Dec 5]. Available from: https://foundationprogramme.nhs.uk/programmes/2-year-foundation-programme/ukfp/introduction/ Hope C, Lund J, Griffiths G, Humes D. Differences in progression by surgical specialty: a national cohort study. BMJ Open. 2022;12(2):e053391. 10.1136/bmjopen-2021-053391 . Kumwenda B, Cleland JA, Prescott GJ, Walker K, Johnston P. Relationship between sociodemographic factors and selection into UK postgraduate medical training programmes: a national cohort study. BMJ Open. 2018;8:e021329. 10.1136/bmjopen-2017-021329 . Ellis R, Scrimgeour DSG, Brennan PA, Lee AJ, Cleland J. Does performance at medical school predict success at the Intercollegiate Membership of the Royal College of Surgeons (MRCS) examination? A retrospective cohort study. BMJ Open. 2021;11:e046615. BMJ Blogs. The personal cost of postgraduate medical exams—are we asking too much of trainees? [Internet]. 2021 Feb 2 [cited 2026 Jan 12]. Available from: https://blogs.bmj.com/bmj/2021/02/02/the-personal-cost-of-postgraduate-medical-exams-are-we-asking-too-much-of-trainees/ Coyle M, Sandover S, Poobalan A, Bullen J, Cleland J. Meritocratic and fair? The discourse of UK and Australia’s widening participation policies. Med Educ. 2020;55:825–39. 10.1111/medu.14442 . Mawdsley A, Magola-Makina E, Willis SC. Towards addressing the awarding gap—using critical race theory to contextualise the role of intersectionality in Black pharmacy student attainment. Med Educ. 2024;58(10):1235–46. 10.1111/medu.15460 . Gutman LM, Younas F. Understanding the awarding gap through the lived experiences of minority ethnic students: an intersectional approach. Br Educ Res J. 2025;51:990–1008. 10.1002/berj.4108 . Gibson Smith K, Brown C, Jones L, Patel R, Ahmed S, Green J. I’d keep going until somebody said no and nobody ever said no: exploring identity-strengths amongst medical students from widening participation backgrounds. Front Med. 2025;12:1530738. 10.3389/fmed.2025.1530738 . Additional Declarations Competing interest reported. All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest . JCl is the Editor in Chief of Medical Teacher. All authors declare no other financial relationships with organisations that might have an interest in the submitted work in the previous three years and no other relationships or activities that could appear to have influenced the submitted work. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8603363","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597135689,"identity":"3a6c0e65-fea5-4ec6-bbeb-cbc5f3bfcc05","order_by":0,"name":"Emma Fletcher","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABL0lEQVRIie2RMWuDQBTHT4S4XOtWngSSr6AIiYPgVzkJOGnnFArRxS75AC0N/QxCwNkQyNILWYVbLIVMGeyWbr2T0uUszdjhfvDecMeP9/53CCkU/xbf4w3zmvOq0IB37/uq+kWJgAtCoT8KXKho+QWKefMatojAKDD2m/e7l3J0fdgem3YOATK2jY6ppFjPyRq44mJ8O3P2JXOtOpo6jxTCFEe2jmtJsVlSgHaGcAl4YmUlC4saD4ZXORCEYqTjVlIClqzPfMqCK9PPbMUWxYF2SoDMU69iD5NSLEYwn6JlKSN2FXeKloKYIi8GLCk9QsBZ0ti10h1znupoYoksORztzUqOb/L4dUv8sfFAnY/0no3Fi0E79wPTnL01p52kdJDeU/E7Vb+gUCgUij/4As7WZ6MR4fdAAAAAAElFTkSuQmCC","orcid":"","institution":"Medical Schools Council","correspondingAuthor":true,"prefix":"","firstName":"Emma","middleName":"","lastName":"Fletcher","suffix":""},{"id":597135690,"identity":"dc449f12-bf7d-4a6b-b555-c1480ba28612","order_by":1,"name":"Judith Cave","email":"","orcid":"","institution":"University Hospital Southampton NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Judith","middleName":"","lastName":"Cave","suffix":""},{"id":597135691,"identity":"4ab49e87-0f83-4f6e-bc4f-547604996f06","order_by":2,"name":"Jennifer Cleland","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Cleland","suffix":""},{"id":597135692,"identity":"ec0b28d6-aadb-4d7c-899e-5a5a299fa8e1","order_by":3,"name":"Mike Masding","email":"","orcid":"","institution":"UK Foundation Programme Office","correspondingAuthor":false,"prefix":"","firstName":"Mike","middleName":"","lastName":"Masding","suffix":""},{"id":597135693,"identity":"16d27a71-0973-426a-b52e-1e81e7db0fc7","order_by":4,"name":"Sally Curtis","email":"","orcid":"","institution":"University of Southampton","correspondingAuthor":false,"prefix":"","firstName":"Sally","middleName":"","lastName":"Curtis","suffix":""}],"badges":[],"createdAt":"2026-01-14 15:38:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8603363/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8603363/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399525,"identity":"18b88585-474e-4bdb-bbd3-47b59354ab5a","added_by":"auto","created_at":"2026-03-11 12:06:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1519097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEPM deciles of graduates by course type\u003c/strong\u003e. Decile 1 represents graduates in the top 10% and decile 10 in the bottom 10% of EPM point scores.\u003c/p\u003e","description":"","filename":"BMCFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8603363/v1/5dfe3d8de6d644c418bdbfc0.png"},{"id":103630939,"identity":"52ddedf8-e8b6-4e87-b933-bba1fdb0cb85","added_by":"auto","created_at":"2026-02-28 02:12:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":656623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot showing odds ratios for passing the first attempt at a UK Royal College specialty exam. \u003c/strong\u003eOdds ratios are presented for the Graduate Entry Medicine (GEM) and Gateway Medicine (GY) courses relative to Standard Entry Medicine (SEM), adjusted for sociodemographic factors and prior attainment (Model 4).\u003c/p\u003e","description":"","filename":"BMCFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8603363/v1/71ace4a40399cb1d90f103a1.png"},{"id":104399426,"identity":"9c548ce8-85dc-4565-a736-cde7fadd9eaf","added_by":"auto","created_at":"2026-03-11 12:06:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":776357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInverse Kaplan-Meier plot of time to first UK Royal College exam by course type.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"BMCFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8603363/v1/43fe794feae0e93b57b95a12.png"},{"id":104407644,"identity":"f11f53cf-a732-4a89-98f9-8f7adfaab731","added_by":"auto","created_at":"2026-03-11 12:39:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4036933,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8603363/v1/e153f796-b198-4b3a-b094-4651713fe291.pdf"},{"id":103630936,"identity":"5de45653-e3b4-4032-9949-eb9c9e6e1119","added_by":"auto","created_at":"2026-02-28 02:12:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15833,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8603363/v1/580dbddfdeecc5d651bd2500.docx"},{"id":104399431,"identity":"430d5920-02f9-4a59-b773-52a65b56f310","added_by":"auto","created_at":"2026-03-11 12:06:05","extension":"jpeg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":419716,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFileFigureS1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8603363/v1/4ecf4d056f0f9299237b9966.jpeg"}],"financialInterests":"Competing interest reported. All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest. JCl is the Editor in Chief of Medical Teacher. All authors declare no other financial relationships with organisations that might have an interest in the submitted work in the previous three years and no other relationships or activities that could appear to have influenced the submitted work.","formattedTitle":"Differences in doctors’ career progression and attainment by medical degree course type","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe attainment or awarding gap, also known as differential attainment, refers to disparities in achievement between groups that can arise from differences in educational opportunities, demographic and socioeconomic factors [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Evidence from across the globe shows that the awarding gap exists across all stages of medical education and training, spanning multiple specialties and assessment types - including written and clinical exams, and specialty training selection. These gaps have been reported in relation to ethnicity, age, sex, and disability/neurodiversity in many different contexts [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is increasing interest in looking beyond sociodemographic data to also examine whether there are structural and systemic factors within medical education and training that impact on relative performance. For example, there is emerging evidence that there are differences in attainment associated with the type of primary medical course. In the UK this would include: Standard entry to medicine courses (SEM), 5- or 6-year programs courses for students who usually enter directly from school/college; 4-year Graduate entry courses (GEM), for students who already have a university level degree; and Gateway courses (GY), which have an additional year of study at the start of the course for students from educationally or socioeconomically disadvantaged backgrounds. Comparable frameworks exist internationally: there are GEM courses in Australia and GY/pipeline programs in the United States [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEvidence shows that medical schools provide a positive learning environment that can level out prior educational advantages or disadvantages [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The entry requirements for GY programs are lower than those for SEM and GEM courses because applicant achievements are considered against their educational and social circumstances [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, while the gap between GY and SEM students narrows during medical school, with the differences on entry greater than the differences on exit [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e], longitudinal research indicates that the gap between those graduating from GY and SEM courses increases again in postgraduate training [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. This may suggest that GY graduates are disadvantaged later in the postgraduate (PG) medical training pathway, potentially at key progression points such as Royal College examinations (a prerequisite for entry into higher specialty training).\u003c/p\u003e \u003cp\u003eInterestingly, although approximately 8.5% of 2021 UK medical school students entered GEM programs [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], to the best of our knowledge, there has been no direct comparison of the relative progression of GEM, SEM and GY students after graduation. This is an important gap to address: there are conflicting results from comparisons of PG progression for GEM vs SEM students, with some authors reporting delays in passing exams [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], but others reporting no difference [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e] or higher attainment in workplace-based assessments [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, much of this previous work, particularly that with GEM graduates, does not widely control for sociodemographic factors, which are known to be associated with attainment [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese differences and gaps in outcomes are problematic. Enabling equity and fairness and supporting all doctors in achieving their full potential within medical education and training depends on identifying and addressing barriers to progression [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. It is thus critical to identify whether there are systemic disadvantages associated with different course types. This issue has recently come to the attention of policy makers. For example, in the UK, the Medical Schools Council report ‘Fostering Potential’ (2024) emphasises the importance of reducing barriers to a medical career for students from disadvantaged backgrounds from \u003cem\u003e“admission through medical school into postgraduate training”\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur aim in this paper is to explore the relationships among course type, career progression and attainment in UK Royal College exams, controlling for demographic differences between cohorts.\u003c/p\u003e \n\n \n\n \n\n \n\n\n\n"},{"header":"Methods","content":"\u003ch3\u003eStudy context\u003c/h3\u003e\u003cp\u003eIn the UK, medical graduates, known as resident doctors, undergo foundation training, a two-year, work-based training scheme that bridges medical school and specialty training, ensuring that newly qualified doctors develop clinical and professional skills for subsequent training stages [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Doctors may then progress to specialty training before being awarded a certificate of completion of PG training. Progression through this PG journey is dependent upon passing UK Royal College exams and completing satisfactory progress assessments known as the Annual Record of Competency Progression (ARCPs; detailed below). Graduates from all UK course types, GY, GEM and SEM, enter the same PG learning environment.\u003c/p\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis was a retrospective study using data from the UK Medical Education Database (UKMED). The UKMED links undergraduate (UG) and PG educational outcome data and progression data for medical students and resident doctors.\u003c/p\u003e\u003cp\u003eUKMED classifies students according to the course type into which they are enrolled. This means that although students on a gateway programme (GY) usually undertake the same curriculum as SEM students do after the additional year at the start of the programme, they can be identified by their GY classification.\u003c/p\u003e\u003cp\u003eData were provided for UK-domiciled entrants who graduated from a SEM, GEM or GY course at a UK medical school between 2012 and 2022. This timeframe allows for sufficient postgraduate follow-up to capture meaningful progression data, and the dataset includes those who started earlier and may have completed specialty training and those who are still progressing through training. All included graduates had at least one postgraduate outcome.\u003c/p\u003e\u003ch3\u003ePG outcomes\u003c/h3\u003e\u003cp\u003eSeveral key outcome measures were selected to assess differences in PG progression and attainment. These included:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFoundation Programme and specialty training ARCP Outcomes\u003c/b\u003e. The ARCP is an annual review of each doctor in PG training to ensure that they are offering safe, high-quality patient care and are adequately progressing through their training program. A standard ARCP outcome is needed to progress onto the subsequent year of training, including progression from Foundation programme (spanning the first two years postgraduation) to specialty training (following successful completion of the Foundation Programme, where doctors enter a focused training programme in a chosen medical specialty). Although ARCPs have a role in providing feedback to the individual doctor as well as a summative function, for this study outcomes were dichotomised into ‘standard’ or ‘non-standard’, as previously described [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTime taken to sit first UK Royal College exam.\u003c/b\u003e The number of months between graduation and the sitting of each resident doctor’s first exam were calculated. Timely completion of these exams, awarded by the UK Royal Colleges, is necessary to avoid delaying progression. Passing them provides an advantage in obtaining a specialty training program post and is essential to completing higher specialty training.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePass rate at first attempt at a UK Royal College exam.\u003c/b\u003e The names, details and timings of UK Royal College exams differ greatly by specialty. Several exams had a limited number of cases for GY and GEM graduates, therefore the consolidated variable ‘pass on first attempt at any UK Royal College exam’ was used, which included each individual’s first ever recorded attempt at a PG exam. All exams included had at least 5 cases per course type and are listed in the Additional File: Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e. Pass rates were then derived for each course type.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003ch3\u003eSociodemographic variables\u003c/h3\u003e\u003cp\u003eWe controlled for sociodemographic differences previously established as relevant in comparisons of GY and SEM undergraduates and postgraduates and more widely in medical education [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The following variables were included:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSex.\u003c/b\u003e Gender data were not available, as UKMED currently records only sex, with options limited to male and female.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEthnicity.\u003c/b\u003e Ethnicity was included at a ‘high level’ and was grouped as Asian/Asian British, Black/Black British, Mixed, Other, and White. This was chosen over dichotomising into Minority Ethnic and White, due to the large variations in the ethnic backgrounds for each course type. For instance, 21% of GY graduates were Black, compared to less than 3% of GEM or SEM graduates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAge at graduation.\u003c/b\u003e Age at graduation was grouped into \u0026lt; 29 or ≥ 29, as this cut-off has been used by previous authors [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGraduate on entry.\u003c/b\u003e Indication as to whether a previous degree had been completed prior to medical school, or not. This variable is essential as applicants with prior degrees can also enter SEM courses. There is a strong correlation between age and being a graduate on entry.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIndex of Multiple Deprivation (IMD) group and Participation of Local Area (POLAR) group.\u003c/b\u003e IMD and POLAR are postcode-based measures which assess relative socioeconomic disadvantage and participation into higher education, respectively. Both were set as missing for all individuals who entered medical school as a graduate, as the address given on application was likely not their parental home.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFirst in family to attend university\u003c/b\u003e. Indication of whether either parent holds a degree, or not.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSecondary school type.\u003c/b\u003e Grouped by whether state- or private-funded education.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003ch3\u003ePrior attainment\u003c/h3\u003e\u003cp\u003eThe medical school performance component of the Educational Performance Measure (EPM) was used as a measure of prior attainment. The EPM was a national measure used by the UK Foundation Programme Office (UKFPO) in combination with a Situational Judgement Test to rank graduates for the Foundation Programme allocation between 2012 and 2022. EPM has been widely used as a measure of prior attainment in other studies examining progression through medical education and training [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. EPM was available for \u0026gt; 98% of graduates in the dataset.\u003c/p\u003e\u003cp\u003eFrom 2013, all graduates were ranked within their year and medical school into EPM deciles, whereas in 2012, applicants were ranked into quartiles. To allow cases from 2012 and the later years to be combined in one analysis, EPM quartiles and deciles were converted to normal deviate scores (as per Garrud and McManus [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]).\u003c/p\u003e\u003ch3\u003eMissing data\u003c/h3\u003e\u003cp\u003eMissing data were imputed by chained equations using the \u003cem\u003emice\u003c/em\u003e package in R. Predictive mean matching was employed as the imputation method, with all other variables used as predictors. Multiple imputations were generated, and the most plausible model was selected.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll analyses were conducted in R Studio (version 4.4.0) within the University of Dundee’s HIC TRE Safe Haven. χ2 tests were used to assess the relationships between course type and PG measures. Any significant associations (after Bonferroni correction for multiple testing) were taken through into a multivariate analysis. A Spearman's rank correlation coefficient matrix was constructed to assess any monotonic relationships between variables (Additional File: Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e), and a cut-off threshold of 0.4 was used, such that if it was exceeded, then only one variable from each pair would be entered into the model. Logistic regression models were created to predict outcomes by course type, with further models controlling for demographics and EPM. The goodness of fit of each model was subsequently assessed using the Hosmer-Lemeshow test (p \u0026gt; 0.05) and significance of independent variables was assessed using a χ2 test (p \u0026lt; 0.05).\u003c/p\u003e\u003cp\u003eCohen’s d was calculated to measure the effect size of EPM decile scores between course types. A time-to-event graph (inverse Kaplan-Meier) was used to plot the time in months between starting foundation training and first sitting of a UK Royal College examination for each course type.\u003c/p\u003e\u003ch3\u003ePatient and public involvement\u003c/h3\u003e\u003cp\u003eNo patients or members of the public were involved in this study.\u003c/p\u003e\u003ch3\u003eEthics\u003c/h3\u003e\u003cp\u003eThe project was approved by the University Research Ethics Committee at the University of Southampton (application no. 92852). No patients or members of the public were involved in the study.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and educational performance variations by course type\u003c/h2\u003e \u003cp\u003eThe demographic data for graduates by course type are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Significant associations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were found between course type and all sociodemographic variables, except for sex (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05). GY graduates were more likely to be from a minoritised ethnic background and disadvantaged socioeconomic background. A greater proportion of GEM graduates than to SEM and GY graduates were White or aged 29 years or older.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1. Postgraduate outcome data availability by course type.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.0699%;\"\u003e\n \u003cp\u003ePG outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003eGEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003eGY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003eSEM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.0699%;\"\u003e\n \u003cp\u003eUK-domiciled graduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e5475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e48190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.0699%;\"\u003e\n \u003cp\u003eAnnual Review of Competency Progression (ARCP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e5045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e41190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.0699%;\"\u003e\n \u003cp\u003eUK Royal College exam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e3785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e29530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 58.0699%;\"\u003e\n \u003cp\u003eUK-domiciled graduate with 1+ PG outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e5055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.9767%;\"\u003e\n \u003cp\u003e41290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and missing data by course type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c2\" namest=\"c1\" rowspan=\"3\"\u003e \u003cp\u003eDemographic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eCourse Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u0026nbsp;or\u0026nbsp;Asian\u0026nbsp;British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u0026nbsp;or\u0026nbsp;Black\u0026nbsp;British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge at graduation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e94.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;=29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIMD*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3-Q5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1-Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePOLAR*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ3-Q5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1-Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParent holds a degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSchool Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eState\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP-values were obtained from χ2 tests with Bonferroni correction for multiple testing. *IMD and POLAR set to missing for all graduate-on entry graduates.\u003c/p\u003e \u003cp\u003eThere were significant differences when comparing the EPM deciles by course type. EPM decile distributions were plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which show an even distribution for SEM, but a left-skewed distribution for GEM and a right-skewed distribution for GY. This indicates that GY graduates are ranked lower overall than their peers upon completion of medical school, whereas GEM graduates are ranked higher. A comparison of the groups revealed a large effect size when comparing between both GEM and SEM (0.81 95% CI 0.84 to 0.78) and GY (1.93, 95% CI 1.84 to 2.02) courses, and a small effect size between SEM and GY (0.28, 95% CI 0.20 to 0.35).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eUnivariate analysis\u003c/h2\u003e \u003cp\u003eThe results of the univariate analysis between course type and each outcome measure are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. A lower proportion of GY graduates passed their first attempt at a UK Royal College exam compared to both SEM and GEM graduates, who had comparable rates. In contrast, the percentage of resident doctors awarded a successful ARCP outcome during Foundation or Specialty Training did not differ by course type (p\u0026thinsp;=\u0026thinsp;1). In contrast, the percentage of resident doctors being awarded a successful ARCP outcome during Foundation or Specialty Training did not differ by course type (p\u0026thinsp;=\u0026thinsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of PG outcomes by course type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePostgraduate outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePassed first attempt at a UK Royal College exam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.8\u003c/p\u003e \u003cp\u003e(69.4\u0026ndash;72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003cp\u003e(46.3\u0026ndash;54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.7\u003c/p\u003e \u003cp\u003e(73.2\u0026ndash;74.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuccessful First Foundation ARCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.5\u003c/p\u003e \u003cp\u003e(99.3\u0026ndash;99.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99.3\u003c/p\u003e \u003cp\u003e(98.8\u0026ndash;99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.6\u003c/p\u003e \u003cp\u003e(99.5\u0026ndash;99.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuccessful First Specialty ARCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003cp\u003e(97.7\u0026ndash;98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003cp\u003e(95.9\u0026ndash;99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98.3\u003c/p\u003e \u003cp\u003e(98.1\u0026ndash;98.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNs represent the total number of available cases per course type. P values were calculated using χ2 tests and adjusted with Bonferroni correction.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate analysis\u003c/h2\u003e \u003cp\u003eThere were significant variations by course type in terms of the proportions of candidates passing a UK Royal College exam on first sitting, so this was included in the multivariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Initial univariate regression analyses (Model 1) evaluated course type as a predictor for each measure, while multivariate regression analyses further adjusted for ethnicity (Model 2), all sociodemographic factors (Model 3) and all sociodemographic factors and prior attainment (Model 4).\u003c/p\u003e \u003cp\u003eThe univariate analysis revealed that compared with SEM, GY and GEM graduates had significantly lower odds of passing their UK Royal College exam on first sitting (OR\u0026thinsp;=\u0026thinsp;0.37 and 0.86, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). For GEM, the association remained significant after adjusting for ethnicity, but after adjusting for all demographic variables the association became non-significant (Odds Ratio (OR)\u0026thinsp;=\u0026thinsp;1.05, p\u0026thinsp;=\u0026thinsp;0.23), suggesting that these variables may account for the observed differences. Interestingly, when the model was further adjusted to include EPM, the association became statistically significant once more (OR\u0026thinsp;=\u0026thinsp;0.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For GY, the association remained significant after each model adjustment (Models 2\u0026ndash;4), with the OR gradually increasing across the models. This suggests that while demographic factors and prior attainment explain some of the observed disadvantages for GY graduates, additional unmeasured or contextual factors may also contribute to the observed lower odds ratio. A forest plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) illustrates the significant difference seen in Model 4 for GEM and GY relative to the SEM course type.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate Logistic Regression of UK Royal College exam First Attempt Pass Rates by Course Type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome measure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCourse Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModel 1 \u0026ndash; Univariate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel 2 \u0026ndash; adjusted for Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eModel 3 \u0026ndash; adjusted for all demographics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eModel 4 \u0026ndash; adjusted for demographics and EPM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePassed first attempt of a UK Royal College exam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e(0.80 to 0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003cp\u003e(0.80 to 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003cp\u003e(0.97 to 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003cp\u003e(0.83 to 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003cp\u003e(0.33 to 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003cp\u003e(0.39 to 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003cp\u003e(0.41 to 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003cp\u003e(0.48 to 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOR\u0026thinsp;=\u0026thinsp;Odds Ratio; 95% CI\u0026thinsp;=\u0026thinsp;95% Confidence Interval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/h2\u003e \u003cp\u003eTime taken for the first attempt at a UK Royal College exam\u003c/p\u003e \u003cp\u003eA significant delay from commencing work as a doctor to sitting the first UK Royal College exam can be seen for GY graduates compared with SEM and GEM graduates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Prior to completion of the Foundation Training Programme (time\u0026thinsp;\u0026asymp;\u0026thinsp;24 months), 39.0% and 38.3% of SEM and GEM graduates, respectively, had sat their first medical PG exam, compared to 27.4% of GY graduates. It took an additional year for half of the GY graduate cohort to sit their first medical PG exam (time\u0026thinsp;=\u0026thinsp;41 months, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared with SEM (time\u0026thinsp;=\u0026thinsp;29 months) and GEM (time\u0026thinsp;=\u0026thinsp;30 months).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/h2\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to explore postgraduate (PG) attainment by course type. Our findings strongly suggest that, in the UK, graduates from Gateway (GY) courses experience greater challenges with medical PG examinations in comparison to standard entry medicine (SEM) graduates, and that these challenges are not purely a result of differences in either ethnicity, previous academic attainment or socioeconomic background. Graduates from graduate entry (GEM) courses also experience challenges, although the magnitude is smaller.\u003c/p\u003e \u003cp\u003eSpecifically, we found differences between SEM, GEM and GY graduates with respect to pass rates on the first sitting of a UK Royal College exam and the time from graduation to sitting these exams. Graduates of GEM and GY courses were significantly less likely to pass PG exams at the first attempt and GY graduates took a median of 11 months longer before attempting a UK Royal College exam for the first time. These differences were found despite controlling for sociodemographic variables, prior academic achievement and ethnicity. In contrast, there was no difference between groups in terms of performance on ARCPs, suggesting that workplace-based assessments do not contribute to the attainment gap.\u003c/p\u003e \u003cp\u003eWe have also provided evidence that GY courses are an effective method for broadening the demographics of the medical workforce. GY graduates are more likely to be from a minority ethnic group, from less affluent backgrounds, less likely to have a parent with a degree, and less likely to have attended a fee-paying school. GEM graduates were more likely to be from a white background and be aged 29 years or older. This aligns with previous studies comparing GY and SEM students and graduates [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] but this is the first time the demographics of graduates from the three different types of courses have been compared directly.\u003c/p\u003e \u003cp\u003eThe causes for variable journeys through the same PG learning environment are unknown but are likely to be complex and multifactorial. We propose three possible and potentially overlapping causes: the medical school course itself; the candidates and their life outside of work; and the PG learning environment including the workplace itself, teaching by colleagues and the UK Royal College examinations. We have shown that the candidates differ between course types in terms of demographics. These differences may be associated with different challenges and commitments outside work; for example, in terms of financial and family commitments. In turn, different commitments may impact on candidates\u0026rsquo; ability to accommodate the financial and time commitments involved in preparing for and sitting PG exams [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We know that implicit bias within the learning environment is a cause of other attainment gaps in PG medicine [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Although our data cannot confirm this intersectionality, social categorisations such as race, class and sex are interconnected, leading to overlapping and complex patterns of disadvantage, which may be a contributing factor to our findings [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has identified differences in progression related to course type. We have shown that these challenges are not purely a result of differences in ethnicity, previous academic attainment or socioeconomic background. Further research is urgently needed to understand the challenges GEM and GY doctors experience with sitting and passing UK Royal College exams, and to seek to explain the attainment gap seen in exams but not in workplace-based assessments. Postgraduate learning providers and the Royal Colleges need this information so that they can work towards providing an equitable training experiences for graduates of all course types. Moreover, we urge those working in other contexts with different types of medical courses running in parallel (e.g., Australia with SEM and GEM courses, the USA with GY and SEM courses) to carry out similar analyses to identify whether differences in progression related to course type are a widespread issue.\u003c/p\u003e \u003cp\u003eThe strengths of this study are that it is a large cohort (N\u0026thinsp;=\u0026thinsp;47,115), is country-wide, and covers multiple graduating cohorts of doctors. Individual demographics were controlled for, and the study used relevant and objective outcome measures. Limitations include that this study is purely observational, and confounding factors such as the use of EPM as a measure of prior attainment could also be challenged as institutional approaches to determining the EPM scores differ slightly. For example, some institutions applied a single EPM across all course types, whereas others applied the EPM by course type. Additionally, only the course performance ranking component of the EPM was used, because of large variations in additional degrees and publications by course type and their associated financial and time commitments, which could introduce inequity, particularly for GY graduates.\u003c/p\u003e \u003cp\u003eIn conclusion, establishing different course types, especially gateway courses, has increased socioeconomic diversity in medical student and resident doctor populations. Different course types have introduced an attainment gap on entry to medical school which although is seen to reduce on exit, widens again during postgraduate training. In this study we have provided the necessary evidence to show that there is an issue. These findings must be used to help inform improvements in postgraduate training programs and their working environments to enable a fair and equitable experience for all resident doctors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARCP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnnual Review of Competency Progression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEducational Performance Measure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGraduate Entry Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGY\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMedicine with a Gateway Year\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndex of Multiple Deprivation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePOLAR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eParticipation of Local Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Entry Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUKFPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUK Foundation Programme Office\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUKMED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUK Medical Education Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was approved by the University Research Ethics Committee at the University of Southampton (application no. 92852). No patients or members of the public were involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were accessed through the HiC Dundee Safe Haven under the\u003c/p\u003e\n\u003cp\u003eUKMED application process. Access was granted following approval of our application and\u003c/p\u003e\n\u003cp\u003esigning of a Data Sharing Agreement (DSA). Data were provided in pseudonymised form\u003c/p\u003e\n\u003cp\u003eand were stored in a secure environment; therefore, they cannot be shared publicly.\u003c/p\u003e\n\u003cp\u003eResearchers who meet the criteria for access can request access via the UKMED\u003c/p\u003e\n\u003cp\u003eapplication process and use of the HiC Dundee Safe Haven.\u003c/p\u003e\n\u003cp\u003eThe source of the data used in this project is the UK Medical Education Database (UKMED) [project number \u0026lsquo;P194\u0026rsquo;], with the extract generated on 24/05/2024. Approved for publication on 6th June 2025. I am grateful to UKMED for the use of these data. However, UKMED bears no responsibility for their analysis or interpretation. The data includes information derived from that collected by the Higher Education Statistics Agency Limited (\u0026ldquo;HESA\u0026rdquo;) and provided to the GMC (\u0026ldquo;HESA Data\u0026rdquo;), source: HESA Student Record 2006 to 2018. Copyright Higher Education Statistics Agency Limited. The Higher Education Statistics Agency Limited makes no warranty as to the accuracy of the HESA Data and cannot accept responsibility for any inferences or conclusions derived by third parties from data or other information supplied by it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest. JCl is the Editor in Chief of Medical Teacher. All authors declare no other financial relationships with organisations that might have an interest in the submitted work in the previous three years and no other relationships or activities that could appear to have influenced the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEF is the guarantor of the study. EF, SC and JCa conceived the study. EF performed the statistical analysis. EF, SC, JCa and MM interpreted the data. EF, SC, JCa, MM and JCl drafted and critically revised the manuscript. All authors approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMountford-Zimdars A, Sabri D, Moore J, Sanders J, Jones S, Higham L. Causes of differences in student outcomes (HEFCE). London: HEFCE; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWikaire E, Curtis E, Cormack D, et al. Predictors of academic success for Māori, Pacific and non-Māori non-Pacific students in health professional education: a quantitative analysis. Adv Health Sci Educ Theory Pract. 2017;22(2):299\u0026ndash;326.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeherani A, Hauer KE, Fernandez A, King TE, Lucey C. 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Front Med. 2025;12:1530738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2025.1530738\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2025.1530738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Attainment gap, Postgraduate medical education, Widening participation in medicine","lastPublishedDoi":"10.21203/rs.3.rs-8603363/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8603363/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eReducing barriers to a medical career for those from disadvantaged backgrounds is a priority for medical educators, regulators and governments. However, evidence indicates attainment gaps for graduates from different course types. It is essential to determine whether any cohort faces systematic disadvantage during training. This paper examines the postgraduate (PG) performance of graduates of the three main UK undergraduate medical degree course types.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis was a retrospective study using the UK Medical Education Database (UKMED). Data were included from doctors who graduated between 2012 and 2022, were UK domiciled, with at least one PG outcome: Annual Review of Competency Progression (ARCP) outcomes (assessment within the training workplace), performance on first attempt at a UK Royal College examination, and time taken to sit the examination. Multivariate logistic regression analyses evaluated PG attainment for graduates from Standard, Graduate or Gateway Programs, while adjusting for prior attainment and demographic factors including ethnicity, area measures of disadvantage and first-in-family to attend university.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eData were available for 41,290 standard entry (SEM), 5,055 graduate entry (GEM), and 770 gateway (GY) graduates. Significant associations were found between course type and all sociodemographic variables except for sex. SEM graduates were more likely to pass their first UK Royal College exam than GEM or GY graduates (ORs 0.86 and 0.37 respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both comparisons). Adjusting for all sociodemographic variables increased the OR to 0.49 for GY graduates and returned it to 1.05 for GEM graduates. The median time to sit the first Royal College exam was longer for GY graduates (41 months compared to 29 for SEM and 30 for GEM, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no differences in ARCP outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThere are significant differences in postgraduate progression for UK Royal College exam performance and time taken to sit exams associated with undergraduate course type, particularly GY courses. These differences remain after accounting for prior attainment and socioeconomic factors, so may be associated with the training environment. Postgraduate learning providers, and those who regulate postgraduate examinations and training must now examine what underpins these differences, to ensure equitable opportunities to progress for all medical graduates.\u003c/p\u003e","manuscriptTitle":"Differences in doctors’ career progression and attainment by medical degree course type","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-28 02:12:07","doi":"10.21203/rs.3.rs-8603363/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"94925408419669888094317780169905290521","date":"2026-03-06T14:59:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-25T14:24:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-30T12:12:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-30T11:24:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-30T11:21:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-01-14T15:28:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d94cdd66-b747-4b13-a76c-7b538809301b","owner":[],"postedDate":"February 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-28T02:12:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-28 02:12:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8603363","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8603363","identity":"rs-8603363","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}