What personal characteristics contribute to successful training outcomes?

The surgical workforce is increasingly diverse. Surgery benefits from, and relies upon, graduates from many different countries and backgrounds. In recent years, the demographic of surgical trainees has changed, with more women and non-UK-trained graduates entering the specialty.

Worldwide, surgery remains dominated by men and those from more affluent socioeconomic backgrounds.13 Globally, women in surgery are underrepresented and this has been highlighted in the world media with campaigns such as #ILookLikeASurgeon and the ‘New Yorker Cover Challenge’.4, 5 In the UK and Australia, men make up 88% of consultant surgeons;1, 2 80% of US surgeons are men.6 This is despite the fact that women now outnumber men at medical school in the UK and the United States.7, 8 However, the number of women training in surgery is increasing; over one-third of UK and US surgeons in training are women,9, 10 which, with time, will substantially increase the proportion of female consultant surgeons. In 2017, 10.7% of UK doctors in training across all specialties were working less than full time.11 The majority of less than full time trainees are women,12 so it is reasonable to assume that with an increasingly female workforce, the proportion of surgical trainees who intend to work less than full time may increase.

The UK Equality Act 2010 legally protects people from discrimination in the workplace on the basis of protected characteristics (age, sex, disability, gender reassignment, marriage, pregnancy, race, religion or sexual orientation).13 Differential attainment refers to the differences in performance between groups with and without protected characteristics.14 The impact of differential attainment on postgraduate medical outcomes has been of interest for many years, but this has not been studied in general surgical training. To retain a highly skilled workforce within surgery, it is essential to ensure fair and equal assessment of progression for all, regardless of protected characteristics or working pattern. This is the first study to assess the impact of demographic and training specific factors on trainee progression through annual review of competence progression (ARCP) panels in general surgical specialty training in the UK.

The aim of the study was to identify demographic factors, including protected characteristics, and training-specific factors which predict a non-standard ARCP outcome during general surgical specialty training in the UK.


This was a longitudinal cohort study using anonymised data provided by the UK Medical Education Database (UKMED). UKMED is a collaborative research database containing longitudinal sociodemographic and educational information for UK and international medical students, which provides a platform for collating data on the performance of UK medical students and trainee doctors across their education and future career.15 It was developed with support from the General Medical Council (GMC), the Medical Schools Council and the University Clinical Aptitude Test Consortium. The database includes all students commencing medical school in the UK since 2002. From 2012 onwards, students graduating from non-UK medical schools and entering UK postgraduate training at any level are included. As of November 2017, the database contained information on 110,783 medical students starting studies between 2002 and 2017 and continues to collect data after graduation. The data in UKMED links with information held by the GMC, with ARCP outcomes and demographic information available for each graduate. ARCP outcomes are collected from 2010. The UKMED data can be modelled to assess the relationship between predictors of success and progression throughout surgical training.

The outcome measure in this study was ARCP outcome. The ARCP is the way in which doctors are reviewed to ensure that they meet standards of safe patient care and are progressing adequately in their training. Successful progression to the next stage of training is dependent on the trainee meeting a number of specialty-specific targets, such as the requisite number of workplace-based assessments, attendance at relevant courses and operative logbook experience. Possible ARCP outcomes are summarised in Table 1. ARCP outcome 1 is considered as standard and allows the trainee to progress into the next year of training, having achieved progress and the development of competences at the expected rate. Outcomes 2 and 3 require the development of competences, with or without additional training time. Outcome 4 results in release from the training programme. Less than full time trainees are held to the same ARCP standards as full time trainees.


Table 1 Annual review of competence progression outcomes.

Table 1 Annual review of competence progression outcomes.

Outcome Description
1 Satisfactory progress: achieving progress and the development of competences at the expected rate
2 Development of specific competences required: additional training time not required
3 Inadequate progress: additional training time required
4 Released from training programme: with or without specified competences
5 Incomplete evidence presented: additional training time may be required
6 Gained all required competences: will be recommended as having completed the training programme
(core of specialty) and if in a run-through training programme or higher training programme, will be recommended for award of a CCT or CESR/CEGPR
7 Fixed term posts (split into 1-4 as above)
8 Out of programme for clinical experience, research or a career break

aStandard outcomes are annual review of competence progression (ARCP) outcomes 1, 6, and 7.1. Non-standard outcomes are ARCP outcome 2, 7.2, 3 and 4. Outcome 5 is awarded when insufficient evidence is presented with a window of 2 weeks for the trainee to provide missing evidence before a standard or non-standard outcome is awarded, Outcome 8 is awarded when out of programme and neither this nor any outcome 7 were included in the analysis.

CCT, certificate of completion of training; CEGPR, certificate of eligibility for GP registration; CESR, certificate of eligibility for specialist registration.

ARCP outcomes were coded into an ordinal scale following the method developed by Tiffin et al:16


group 1: ‘satisfactory progress/training completed’: ARCP outcomes 1, 6, and 7.1


group 2: ‘insufficient evidence presented’: ARCP outcome 5


group 3: ‘targeted training required (no extended time)’: ARCP outcome 2 and 7.2


group 4: ‘extended training time required/left programme’: ARCP outcome 3 or 4.

Group 1 was considered as standard and groups 2, 3 and 4 non-standard in this analysis. Any ARCP outcomes coded as ‘out of programme’ (outcome 8) were removed. Outcomes 7.1 and 7.2 only applied to fixed-term posts (not training posts), so were not included in the analysis.

The cohort of interest is general surgery trainees in specialty training (ST3–ST8), with an ARCP outcome recorded between 2010 and 2017. All ARCP outcomes associated with general surgery were selected. In the event of more than one ARCP outcome within 12 months, the first chronological outcome was used. This allows us to consider ARCP outcomes as independent of each other and accounts for the fact that people enter and leave the cohort at different time points.

Variables were selected to include a mixture of demographic, socioeconomic and training specific factors. Age at ARCP was the only continuous variable. Region of primary medical qualification was categorised into UK, European Economic Area (EEA) and international medical graduates (IMG). Geographical location of training was characterised into home nation and thence divided into Health Education England region within England. In the case of pre-medicine socioeconomic status, multiple variables existed within the dataset. However, there were no measures of pre-medicine socioeconomic status for non-UK graduates and there was a high proportion of missing data. Of the available measures, the Index of Multiple Deprivation was selected. This index is a standardised system that characterises areas by social deprivation, which is calculated by taking into account multiple factors such as income and employment domains. The score is calculated differently for each of the UK countries.17 The score ranks each small area within England, Northern Ireland, Scotland or Wales, from least deprived (1) to most deprived (5). The postcode on application to medical school is used to calculate the score, so only values for UK graduates are available. Thus, a non-UK category was added to account for EEA and IMG and a missing category, which accounted for missing data from UK graduates only. After adding in the non-UK category, this was the measure with the least missing data and was therefore selected.

Missing data were coded as a separate category within each variable, to avoid reducing the sample size in the regression model. Missing data per each category are listed in Table 2. Some of the missing data were due to the measurement of certain variables being UK specific and therefore not present for non-UK graduates. It was not possible to look at other protected characteristics available within UKMED (religion and sexual orientation), as data on these were only collected from 2016 and so were missing from over 85% of the whole cohort of interest.


Table 2 Demography and personal characteristics by annual review of competence progression outcome.

Table 2 Demography and personal characteristics by annual review of competence progression outcome.

Characteristic Group
1: Satisfactory
(n=7, 859; 77%)
2: Insufficient
(n=1005; 10%)
3: Targeted time training
(n=710; 7%)
4: Extended training time/left programme
(n=562; 6%)
Male: Female 5325 : 2540 (68%) 726 : 280 (72%) 500 : 210 (70%) 380 : 180 (68%)
Age at ARCP (years)a 36 (28-51) 36 (28–47) 36 (29-48) 38 (30-56)
Place of primary medical degree:        
UK 6350 (81%) 800 (80%) 580 (81%) 415 (74%)
EEA 205 (3%) 35 (3%) 20 (3%) 20 (3%)
IMG 1305 (16%) 170 (17%) 110 (16%) 130 (23%)
White 2715 (35%) 435 (43%) 220 (31%) 135 (24%)
BME 1095 (14%) 210 (21%) 155 (22%) 65 (11%)
Missing 4050 (51%) 360 (36%) 335 (47%) 365 (65%)
Less than full time:        
Yes 1,065 (14%) 105 (10%) 95 (13%) 75 (13%)
No 6,480 (82%) 900 (89%) 595 (84%) 460 (82%)
Missing 315 (4%) 5 (1%) 20 (3%) 30 (5%)
Graduate on entry:        
Yes 365 (5%) 50 (5%) 45 (6%) 25 (4%)
No 7,495 (95%) 955 (95%) 665 (94%) 535 (96%)
Academic trainee:        
Yes 345 (4%) 50 (5) 30 (4%) 20 (3%)
No 7,510 (96%) 955 (95%) 685 (96%) 545 (96%)
Military trainee:        
Yes 110 (2%) 25 (3%) 5 (1%) 10 (2%)
No 7,750 (98%) 980 (97%) 705 (99%) 550 (98%)
Index of multiple deprivation:        
1 (least deprived) 1,255 (16%) 215 (21%) 95 (14%) 50 (9%)
2 755 (10%) 125 (12%) 80 (11%) 35 (6%)
3 480 (6%) 90 (9%) 60 (9%) 30 (5%)
4 285 (4%) 35 (4%) 40 (5%) 25 (4%)
5 (most deprived) 135 (1%) -b 25 (4%) -b
Non-UK 1,510 (19%) 205 (20%) 130 (18%) 145 (26%)
Missing 3,440 (44%) 320 (32%) 275 (39%) 270 (48%)

aMedian and interquartile range. Numbers rounded as per HESA so may not equal 100%.

bHead counts based on less than 22.5 individuals suppressed as per General Medical Council policy.

ARCP, Annual review of competence progression; BME, black and minority ethnic; EEA, European Economic Area (excluding UK); IMG, international medical graduate (rest of world, excludes UK and EEA).

Descriptive statistics were performed to describe the satisfactory and non-standard cohorts. Variables of interest were tabulated to ensure sufficient numbers of trainees per category. Data were rounded to ensure anonymity, following guidance from the Higher Education Statistics Agency.18 A multilevel mixed-effects ordinal regression was performed with individual trainees as the first level and training region as the second level to obtain unadjusted odds ratios. Geographical location of training was characterised into home nation and thence divided into Health Education England region within England. Odds ratios (OR) and 95% confidence intervals (CI) were reported, controlled for clustering by region of training. To obtain p values, Wald’s test was used for binary variables and likelihood ratio tests for categorical variables.

Age at ARCP and sex were considered a priori confounders. A multiple multilevel mixed-effects ordinal regression was performed including age at ARCP, sex and the significant variables from the univariate analysis (place of medical qualification, less than full time training, academic training, military training and index of multiple deprivation). The regression was performed in a backward stepwise manner. All interaction terms were checked at each stage of the model. p values less than 0.05 were taken as significant. All analyses were performed using STATA version 15.


The analysis included 2,730 trainees in higher general surgical training, comprising 10,136 ARCP outcomes. Sixty-eight percent (1,857/2,730) were male. The median age at ACRP was 36 years (interquartile range 28–56 years). The majority of trainees (77%) had attended a UK medical school. Twenty-three percent of trainees were awarded a non-standard outcome at any point in training. Between 2010 and 2017, 0.7% of trainees were released from programme (ARCP outcome 4), representing 2.5% of all non-standard outcomes. The proportion of male trainees remained relatively constant across groups 1–4 (Table 2). Proportions of academic, military, graduate on entry and less than full time remained similar across all groups. While the proportion of non-UK graduates was greatest in group 4 (36% compared with 19% in group 1), this group had the smallest proportion of trainees coming from the least deprived areas.

Older age at ARCP, non-UK graduates, non-academic and non-military training and index of multiple deprivation score had significantly increased risk of a non-standard ARCP outcome in the univariate logistic regression analysis (Table 3) after adjustment for clustering by training region. For each year increase in age at ARCP, there was a 0.8% increased risk of non-standard outcome (OR 1.08, 95% CI 1.07–1.10). Non-UK graduates were more likely to receive a non-standard outcome than those who trained in the UK (EEA: OR 1.31, 95% CI 0.99–173; IMG: OR 1.13, 95% CI 1.00–1.28). Trainees from the most deprived social category had more than 50% increased risk of receiving a non-standard outcome compared with the least deprived (OR 1.54, 95% CI 1.11–2.16). Female sex and less than full time training were protective factors in the univariate analysis. Female trainees were 17% less likely to receive a non-standard outcome than males (OR 0.83, 95% CI 0.75–0.93). Those who were in less than full time training at the time of ARCP were 4% less likely to receive a non-standard outcome than full time trainees during general surgical specialty training (OR 0.96, 95% CI 0.77–1.19). Military trainees were 16% less likely to receive a non-standard outcome than non-military trainees (OR 0.84, 95% CI 0.49–1.44).


Table 3 Univariate and multiple multilevel mixed-effects ordinal logistic regression.

Table 3 Univariate and multiple multilevel mixed-effects ordinal logistic regression.

Unadjusted OR
(95% CI)
p value Adjusted OR
(95% CI)a
p value
Female sex 0.83 (0.75-0.93) 0.001 0.89 (0.80–0.99) 0.045
Age at ARCP 1.08 (1.07-1.10) <0.001 1.13 (1.11–1.15) <0.001
Plaoe of primary medical degree:        
UK 1 0.039 1 0.148
EEA 1.31 (0.99-1.73)   1.38 (0.98–1.95)  
IMG 1.13 (1.00-1.28)   1.10 (0.87–1.40)  
Less than full time:        
No 1 <0.001 1 <0.001
Yes 0.88 (0.76-1.01)   0.85 (0.73–0.99)  
Missing 0.61 (0.45-0.83)   0.58 (0.43–0.79)  
Graduate at entry 1.09 (0.88-1.36) 0.411    
Academic trainee:        
No 1 0.045 1 0.366
Yes 0.96 (0.77-1.19)   0.90 (0.73–1.13)  
Missing 0.88 (0.80-0.97)   0.54 (0.45–0.64)  
Military trainee:        
No 1 0.039 1 <0.001
Yes 0.84 (0.49-1.44)   0.90 (0.52–1.54)  
Missing 0.88 (0.80-0.97)   0.55 (0.46–0.65)  
Index of multiple deprivation:        
1 (Least deprived) 1 <0.001 1 0.041
2 1.51 (0.95-1.40)   1.09 (0.90–1.33)  
3 1.41 (1.14-1.74)   1.34 (1.08–1.66)  
4 1.47 (1.13-1.90)   1.29 (0.99–1.67)  
5 (most deprived) 1.54 (1.11-2.16)   1.49 (1.06–2.08)  
Non-UK 1.23 (1.05-1.45)   1.14 (0.90–1.44)  
Missing 0.97 (0.84-1.12)   1.11 (0.92–1.33)  

ARCP, Annual review of competence progression; EEA, European Economic Area (excluding UK); IMG, international medical graduate (rest of world, excludes UK and EEA).

aAdjusted for sex, age at ARCP, place of medical degree, less than full time training, academic training, military training and index of multiple deprivation.

Older age at ARCP and index of multiple deprivation remained significant predictors of a non-standard ARCP outcome in the multivariable logistic regression (Table 3). Female sex, military trainees and less than full time training remained protective factors. Place of medical degree and academic training were no longer significant as a predictor of outcome. For each year increase in age at ARCP, trainees were 13% more likely to receive a non-standard outcome (OR 1.13 95% CI 1.11–1.15). Trainees from the most deprived areas were 49% more likely to receive a non-standard outcome compared with the least deprived (OR 1.49 95% CI 1.06–2.08). Females were 11% less likely to receive a non-standard outcome than males (OR 0.89 95% CI 0.80–0.99). Trainees who worked less than full time were associated with a 15% decreased risk of a non-standard ARCP outcome (OR 0.85 95% CI 0.73–0.99). Military trainees were 10% less likely to receive a non-standard outcome than civilian trainees (OR 0.90 95% CI 0.52–1.54).


This is the largest published study investigating the effect of personal characteristics on progression through general surgical specialty training. We have shown that after adjustment for other factors, age at ARCP and index of multiple deprivation may predict non-standard outcome at ARCP, while female sex and less than full time training are protective.

The strength of this study is that it is the first of its kind to closely examine the impact of demographic and training specific variables on ARCP outcomes in general surgery on a national scale. UKMED has provided a large cohort in which the data is recorded by the GMC and other regulatory bodies and so is not reliant on voluntary completion of surveys. One of the main limitations of this study is the inability to include a measure of prior educational achievement or postgraduate examination success in the model. This is due to large amounts of missing data for these variables. Owing to the unreliability of grade of training within the database, it was not possible to analyse outcomes by training year. Due to the observational nature of this study, it is not possible to account for variables not contained within the dataset.

Our finding that male surgical trainees are more likely to receive a non-standard ARCP outcome is echoed in other studies; this is despite the suggestion that surgery is not female friendly and is perceived to have a male bias.1921 Across all medical specialties, men have a 40% increased likelihood of a non-standard outcome.16, 22 Similarly, a study looking at medical specialty trainees found that men were significantly more likely to receive ARCP outcomes 3 and 4 (extended training time and left programme) than women (7.1% vs 3.8%, p < 0.001).23

Country of primary medical qualification was not associated with non-standard ARCP outcomes. This agrees with an earlier study that found no difference in the odds of receiving a satisfactory ARCP outcome between UK graduates and overseas graduates in UK surgical training.22 Conversely, a study of more than 53,000 doctors across medical training as a whole, investigating the use of the Professional and Linguistic Assessments Board examination, found that international graduates are less likely to receive a satisfactory outcome at ARCP than UK graduates across medicine as a whole.16

Compared with other specialties, the number of older doctors in general surgery is increasing, with a 16% increase between 2012 and 2017.1 Our finding that older age at ARCP predicts a greater chance of receiving non-standard ARCP outcomes is in line with other studies.24, 25 A study of over 38,000 UK graduate doctors with details regarding ARCP outcome provided by the GMC found that older doctors were more likely to receive non-standard ARCP outcomes than their younger counterparts.24 Older trainees were also more likely to be released from the programme. Smith and Tiffin found that older trainees were less likely to receive a satisfactory outcome at ARCP than younger trainees.25 Furthermore, older residents had higher rates of burnout than younger doctors according to an international meta-analysis,26 which may contribute to difficulties meeting ARCP requirements. The reasons behind our findings are likely to be multifactorial and may include factors outside work including family, other external pressures and perhaps illness.

To increase self-reliance on the supply of medical graduates in the UK, the UK Medical Workforce Standing Advisory Committee suggested establishment of medical courses for graduates. Previous studies have shown that older doctors do better or comparably to school leaver entrants at medical school,27, 28 however our results suggest that this is not sustained in specialty surgical training.

Trainees and medical students from deprived socioeconomic backgrounds are in the minority in the UK and America.29 We found the index of multiple deprivation to be a significant predictor. A 2015 report of core medical trainees found that those from the most deprived socioeconomic background received the highest proportion of unsatisfactory ARCP outcomes.30 This trend was echoed in our sample of general surgery trainees, however the results should be interpreted with caution due to missing data and the lack of a measure for non-UK graduates.


Female sex and less than full time were protective factors for standard ARCP outcome after adjustment. However, older age at ARCP and index of multiple deprivation were significantly associated with non-standard ARCP outcomes. Older trainees may need additional support throughout training to ensure career progression. Further studies investigating the reasons for the differences found are required using logbook and assessment data.


Source: UKMED 3506 extract generated on 26/11/2018. I am grateful to UKMED for the use of these data. However, UKMED bears no responsibility for their analysis or interpretation. The data include information derived from that collected by the Higher Education Statistics Agency (HESA) and provided to the GMC. Source: HESA Student Record 2007/2008 and 2008/2009 copyright Higher Education Statistics Agency Limited. The Higher Education Statistics Agency Limited makes no warranty as to the accuracy of the HESA Data, cannot accept responsibility for any inferences or conclusions derived by third parties from data or other information supplied by it.

The data used in this analysis are held by UKMED and can be accessed only via application to UKMED. The study was not pre-registered.

1. General Medical Council. The State of Medical Education and Practice in the UK. London: GMC; 2017. Google Scholar
2. Mclain S, Cook V, Atkinson M, et al. Barriers to women’s participation in surgery: from student to specilaist. ANZ J Surg 2018; 88: 230. Google Scholar
3. Rodriguez Santana I, Chalkley M. Getting the right balance? A mixed logit analysis of the relationship between UK training doctors’ characteristics and their specialties using the 2013 National Training Survey. BMJ Open 2017; 7: e015219-e. CrossrefGoogle Scholar
4. Symplur LLC. #ILookLikeASurgeon healthcare social media hashtag. https://www.symplur.com/healthcare-hashtags/ilooklikeasurgeon (cited March 2021). Google Scholar
5. Mouly F, Favre M. Cover story: Malika Favre’s ‘Operating theatre’. New Yorker 2017; 27 March. Google Scholar
6. de Costa J, Chen-Xu J, Bentounsi Z, Vervoort D. Women in surgery: challenges and opportunities. IJS Global Health 2018; 1: e02. Google Scholar
7. Wolfe L. Statistics on the number of women surgeons in the United States. The Balance 2017. Google Scholar
8. Moberly T. Number of women entering medical school rises after decade of decline. BMJ 2018; 360: k254. Google Scholar
9. Elsey EJ, West J, Griffiths G, Humes DJ. Time out of general surgery specialty training in the UK: A national database study. J Surg Educ 2019; 76: 5564. CrossrefGoogle Scholar
10. Bruce AN, Battista A, Plankey MW et al. Perceptions of gender-based discrimination during surgical training and practice. Med Educ Online 2015; 20: 25923. CrossrefGoogle Scholar
11. General Medical Council. 2017 National training surveys summary report: initial results on doctors’ training and progression. London: GMC; 2017. Google Scholar
12. Harries RL, Gokani VJ, Smitham P, Fitzgerald JEF. Less than full-time training in surgery: a cross-sectional study evaluating the accessibility and experiences of flexible training in the surgical trainee workforce. BMJ Open 2016; 6: e010136. CrossrefGoogle Scholar
13. UK Equality Act 2010. https://www.legislation.gov.uk/ukpga/2010/15/contents (cited March 2021). Google Scholar
14. Regan de Bere S, Nunn S, Nasser M. Understanding Differential Attainment Across Medical Training Pathways: A rapid review of the literature. Final report prepared for The General Medical Council. Plymouth: Plymouth University Peninsula School of Medicine and Dentistry; 2015. Google Scholar
15. Dowell J, Cleland J, Fitzpatrick S, et al. The UK medical education database (UKMED) what is it? Why and how might you use it? BMC Med Educ 2018; 18: 6. CrossrefGoogle Scholar
16. Tiffin PA, Illing J, Kasim AS, McLachlan JC. Annual review of competence progression (ARCP) performance of doctors who passed professional and linguistic assessments board (PLAB) tests compared with UK medical graduates: national data linkage study. BMJ 2014; 348: g2622. CrossrefGoogle Scholar
17. Abel GA, Barclay ME, Payne RA. Adjusted indices of multiple deprivation to enable comparisons within and between constituent countries of the UK including an illustration using mortality rates. BMJ Open 2016; 6: e012750-e. CrossrefGoogle Scholar
18. Higher Education Statistics Agency. Rounding and suppression to anonymise statistics 2018. https://www.hesa.ac.uk/about/regulation/data-protection/rounding-and-suppression-anonymise-statistics (cited March 2021). Google Scholar
19. Fitzgerald JEF, Tang S-W, Ravindra P, Maxwell-Armstrong CA. Gender-related perceptions of careers in surgery among new medical graduates: results of a cross-sectional study. Am J Surg 2013; 206: 112119. CrossrefGoogle Scholar
20. Park J, Minor S, Taylor RA et al. Why are women deterred from general surgery training? Am J Surg 2005; 190: 141146. CrossrefGoogle Scholar
21. Richardson HC, Redfern N. Why do women reject surgical careers? Ann R Coll Surg Engl 2000; 82(9 Suppl): 290293. Google Scholar
22. Tiffin PA, Orr J, Paton LW et al. UK nationals who received their medical degrees abroad: selection into, and subsequent performance in postgraduate training: a national data linkage study. BMJ Open 2018; 8: e023060. CrossrefGoogle Scholar
23. Rothwell CR. A Study to Identify the Factors That Either Facilitate or Hinder Medical Speciality Trainees in Their Annual Review of Competence Progression (ARCP), with a Focus on Adverse ARCP Outcomes. Durham: Durham University; 2017. Google Scholar
24. Pyne Y, Ben-Shlomo Y. Older doctors and progression through specialty training in the UK: a cohort analysis of general medical council data. BMJ Open 2015; 5: e005658 CrossrefGoogle Scholar
25. Smith DT, Tiffin PA. Evaluating the validity of the selection measures used for the UK’s foundation medical training programme: a national cohort study. BMJ Open 2018; 8: e021918. CrossrefGoogle Scholar
26. Low ZX, Yeo KA, Sharma VK et al. Prevalence of burnout in medical and surgical residents: A meta-analysis. Int J Environ Res Public Health 2019; 16: 1479. CrossrefGoogle Scholar
27. Lumb AB, Vail A. Comparison of academic, application form and social factors in predicting early performance on the medical course. Med Educ 2004; 38: 10021005. CrossrefGoogle Scholar
28. Garrud P, McManus IC. Impact of accelerated, graduate-entry medicine courses: a comparison of profile, success, and specialty destination between graduate entrants to accelerated or standard medicine courses in UK. BMC Med Educ 2018; 18: 250. CrossrefGoogle Scholar
29. Cooper RA. Impact of trends in primary, secondary, and postsecondary education on applications to medical school. II: considerations of race, ethnicity, and income. Acad Med 2003; 78: 864876. CrossrefGoogle Scholar
30. General Medical Council. How do Doctors Progress Through Key Milestones During Training? London: GMC; 2016. Google Scholar

Article Metrics

About article usage data:

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean euismod bibendum laoreet. Proin gravida dolor sit amet lacus accumsan et viverra justo commodo. Proin sodales pulvinar tempor. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.

Bookmark and share