Background
There are significant concerns worldwide regarding the number of general practitioners (GPs) available to meet the demands of aging and increasingly complex healthcare populations, with trends over the last 20 years in many high-income countries, including the UK, signifying lower percentages of GPs in the total medical workforce [
1,
2]. Workforce planning reports in the US and UK project worsening shortfalls of primary care providers towards 2030, with a major demand–supply imbalance [
3,
4]. In response, the UK Department of Health issued a mandate to Health Education England to recruit 50% of foundation doctors to general practice (GP) training programmes by 2016 [
5,
6]. However, despite recruitment initiatives [
7], only 33.8% of the pool of successful applicants to specialty training were appointed to GP in 2016 compared to 36.1% in 2012 [
8]. Further, long-term trends consistently show that only approximately 36% of each UK cohort enter the GP register [
9].
Research into the factors associated with medical students’ specialty choice has indicated that decisions to apply for postgraduate training programmes are shaped by a multiplicity of factors and that medical career decision-making is a dynamic and complex process that is not yet fully understood [
9,
10]. Curricular and institutional cultural biases against primary care may exist in medical schools and postgraduate training placements, which could influence subsequent career choice [
11], but variation in entry to GP training across UK medical schools remains relatively unexplored [
12]. Moreover, early clinical placements, longitudinal integrated clerkships and positive role models in primary care are thought to impact positively on intentions to pursue a career in GP [
13]. Survey-based research in a number of countries has focused on identifying both the individual characteristics (sociodemographic, academic and attitudinal) and institutional factors that are associated with self-reported (intended) career preferences of both medical students and early-career doctors [
14‐
20]. However, intentions to follow a particular career pathway are subject to change and may not materialise as actual applications to particular specialty training programmes [
15,
21]. Nevertheless, a recent evaluation of GP selection in the UK showed intentions to be predictive of doctors ending up on the GP register, with the strength of association increasing with the recency of the stated intention [
9]. Importantly, most extant research has only examined the association between career choice and one or a few variables [
10]. Further work is needed to evaluate the extent to which independent factors are associated with actual applications to GP training, to differentiate their relative strengths, and to detect interrelationships between hypothesized predictors.
The UK Medical Education Database (UKMED) has recently been established to provide a secure repository of longitudinal data related to the performance of UK medical students and trainee doctors [
22]. The dataset contains information on sociodemographic factors and measures of academic attainment from secondary school through to postgraduate training, as well as choices made by individuals with respect to undergraduate training, foundation doctor placements and applications to specialty training. This database provides a unique and valuable opportunity to add to the literature on career choice by permitting analysis, for a large cohort of UK doctors, of some of the important factors that are associated with the decision to apply for a place on the nationally recruited GP specialty training programme. The primary aim of this study was to identify significant independent factors associated with the decision to apply for GP training and to delineate typologies of applicants likely to apply. This information is of value to policymakers and educationalists involved with attempts to increase the proportion of GPs in the medical workforce.
Methods
Data, study population and variables
Anonymised data for this study was provided by the UKMED Development Group and accessed remotely by the authors via the Health Informatics Centre Safe Haven at Dundee University (
https://www.dundee.ac.uk/hic/hicsafehaven/). The UKMED Data Dictionary describes the available data that provided longitudinal educational and sociodemographic information on all students entering UK medical schools in the 2007/2008 academic year [
22]. The study population comprised all doctors who applied for specialty training in 2015 and for whom there was no prior application record in the UKMED data, thereby focussing on ‘first time’ applications to specialty training. Application data for earlier and later years was not, at the time of the study, available in UKMED.
We used multivariable logistic regression models to investigate two binary outcomes, namely (1) whether or not the doctor applied to GP specialty training, possibly alongside applications to other specialties, and (2) whether or not the doctor applied solely to GP specialty training.
Independent variables included a range of background factors such as personal, family, academic, medical school, and foundation school (see Additional file
1: Table S1 for distributions and missing values and the UKMED Data Dictionary at
http://www.ukmed.ac.uk/documents/UKMED_data_dictionary.pdf for a full list of data types, descriptions and sources).
Statistical analysis
We conducted univariate analyses to identify missing, unexpected and outlying values, and to assess the data for normality of distribution. For each outcome, we used bivariate tests of association with each independent variable (Fisher’s Exact Test, Pearson’s χ2 test and univariate logistic regression, as appropriate) to inform the construction of multivariable logistic regression models. List-wise deletion excluded cases in which there were missing values for any of the variables in the regression model.
We categorised doctors’ medical school Entry Status as either ‘Graduate entrant to Standard Programme’ , ‘Non-graduate entrant to Standard Programme’ or ‘Entrant to Graduate Programme’. Because data are not widely available for graduate entrants to medical school on the total Higher Education Statistics Agency (HESA) tariff (a score for qualifications held on application to medical school) and total UK Clinical Aptitude Test (UKCAT) score, we conducted analyses of the two outcomes on all doctors (Sample A; n = 7634) and doctors who had been non-graduate entrants to their medical degree programmes (Sample B; n = 5540).
In respect of Sample A and outcomes 1 and 2, preliminary multivariable models revealed the Index of Multiple Deprivation (IMD, a neighbourhood-based measure of social deprivation), the participation of local areas (POLAR2) classification (a neighbourhood-based measure of participation in Higher Education) and type of secondary school attended, to be non-significant. Along with total HESA tariff and total UKCAT score, these three variables were eliminated from the two final models.
In respect of Sample B and outcome 1, a preliminary multivariable model revealed type of secondary school attended, POLAR2, National Statistics Socio-economic Classification (NS-SEC), medical school entry status and total HESA tariff to be non-significant and they were eliminated from this model.
In respect of Sample B and outcome 2, a preliminary multivariable model revealed IMD, POLAR2, NS-SEC, entry status, UK secondary educated, total HESA Tariff, educational performance measure (EPM, a rank-based indicator of attainment during medical school) and the most recent Annual Review of Clinical Progression (ARCP, an indicator of successful progress through postgraduate foundation training) to be non-significant and they were eliminated from this model.
The variables age at entry to medical school, parent degree (whether either parent was a university graduate), income support, and free-school meals (both indicators of low socioeconomic status during schooling) failed to reach significance in any of the preliminary multivariable models and were excluded from all four final models.
We assessed model goodness-of-fit using the Hosmer–Lemeshow test with a
P value greater than 0.05 taken to indicate acceptable fit [
23]. The significance of the effect of individual predictor variables was assessed using a z-test (Wald Test computed as a χ
2 test) with a
P value less than 0.05 taken to indicate statistical significance [
24]. The quality of model classification (sensitivity and specificity of predicted outcomes) was assessed using receiver operating characteristic diagnostics [
25]. The adequacy of model sample size was assessed using the formula N = 10 x k/p, where p is the proportion of negative or positive cases (whichever smallest) in the population and k is the number of independent variables, to indicate the minimum number of cases required [
26].
We examined interaction effects and finally we interpreted the modelling results in relation to the aims of the study, basing our interpretations on predicted probabilities. Typologies, based on profiles of values for the independent variables in a model, enabled insight into which configuration of variables were substantively important in influencing the outcome [
24]. We used Stata 14 for all analyses.
Discussion
Our study has identified influential factors that are independently associated with the likelihood of applying to GP specialty for the cohort of junior doctors applying to core training posts in the UK during 2015. Further, it has responded to calls for a clearer insight into the differences between groups, defined by student characteristics in respect of the career choices they make [
12]. Overall, 43% of the sample applied to GP specialty training, but only 26% applied as a single specialty application. Significant predictors included individual characteristics (sex, ethnicity, IMD, NS-SEC), educational environment (secondary school type, UK vs. non-UK educated, graduate vs. non-graduate entry, medical school attended, foundation school attended), and measures of prior academic attainment (intercalated degree, EPM, UKCAT score). For all measures of academic attainment, a stronger performance was associated with a decreased likelihood of application to GP training. BME females who had been UK secondary school educated and had not intercalated at medical school were most likely to apply to GP training, whereas white male intercalaters, secondary educated outside the UK were the least likely. Graduate entrants to standard entry programmes were more likely than both non-graduate entrants to standard entry programmes and graduate programme entrants, to apply. Interestingly, age was not a significant independent predictor. There was a major effect due to medical school attended, even after correcting for differences due to sociodemographic and educational factors. When graduate entrants were excluded from the analysis, independent school educated, coming from an area of low deprivation and having a high UKCAT score were additional factors associated with lower odds. Our regression analysis highlighted differences due to foundation school as well as medical school attended, indicating that, even after correcting for medical school attended, the learning environment of deaneries independently influences career decisions. As well as identifying the main effect of individual predictors, our analyses have also defined typologies based on predicted probabilities for specified student characteristics, thereby enabling a much more nuanced insight into which configurations of characteristics influence decisions, for whom and how.
A major strength of our study lies in its utilisation of the UKMED database, which pulls together hitherto disparate datasets held by diverse organisations to create a unified picture of doctors’ pathways from school to specialty training. The database contained data on specialty training applications for a complete cohort of UK graduates, thereby providing an opportunity, previously unattainable in UK medical education research, to simultaneously investigate the possible association of 25 educational and sociodemographic factors with the likelihood of applying to GP training. However, there may have been factors that were not included on the UKMED database, such as marital status, which may have an effect. The novelty of the database also brought some limitations. For example, data was only available for a single cohort/year group of UK medical students and the time window was limited. Given the latter, we analysed first-time applications to specialty training only as the current data does not capture the substantial proportion of doctors who apply to GP training more than 5 years after qualifying [
9]. However, such limitations will diminish as the database grows, and our study shows the potential of the UKMED project as an important resource for researchers in this field. Future studies may benefit from the addition of data from more cohorts and greater scope of the included fields. For example, our results show that, even after allowing for demographic factors and measures of educational attainment, the likelihood of applying to GP training varies independently between both medical schools and foundation schools. However, the database currently lacks information that might help explain this variation, such as the extent of clinical exposure to GP experience by medical students or which specialty placements were undertaken during foundation training. However, as previously noted by Davison et al. [
9], expanding the coverage of UKMED will take some time and its scope, particularly regarding medical students’ experiences, interests and intentions, will likely remain limited.
Factors associated with specialty choice have previously been classified into five main categories, namely (1) medical school characteristics (e.g. curriculum structure), (2) student characteristics (e.g. age, personality), (3) student values (personal preference), (4) needs to satisfy (expected income, status, work-life balance), and (5) perceptions of specialty characteristics (e.g. extracurricular or curricular experiences) [
10]. In the main, extant studies have focussed on categories 3, 4 and 5, providing valuable qualitative insights into the mechanisms and motives that influence specialty choice intentions. A major strength of our study is that it is based not on self-reported intentions but on actual specialty applications of a UK national cohort and thus is not prone to response rate and representativeness bias. Choosing ‘applications to GP training’ as the main outcome variable for our analyses rather than ‘entry into GP training programmes’, meant that our results would not be clouded by the selection-related factors that might prevent applicants from actually entering training. Our study complements existing survey-based evidence revealing a wide disparity in the proportion of graduates from individual medical and foundation schools entering GP training [
16,
27]. Our analyses further indicate independent effects due to both medical and foundation school attended that are not solely due to between-school differences in the personal characteristics of their student/trainee cohorts. This finding adds evidence to longitudinal cohort studies assessing ‘attractiveness of GP’ in medical graduates, as reported by Davison et al. [
9], who stated that “
career preferences are malleable both during medical school and following graduation”. However, we were unable to analyse levels of interest in GP amongst study participants, which could explain some of the variation between schools. Our results substantiate the claim, by national reports and student surveys, that differences in career choice are related to variation in the curricula and culture of medical schools [
12,
21,
28] and underlines recommendations for further research into those differences [
29]. We found a significant effect of ‘intercalation’, which supports findings by Lambert et al. [
30] highlighting marked differences between intercalaters (15.3%) and non-intercalaters (25.9%) choosing GP as a career. Querido et al. [
10] found that one-third of the studies included in their systematic review of medical student career choices reported sex as a direct determinant. Our study aligns with this general finding of the main effect of sex, but also adds evidence that the influence of sex on career decisions is likely mediated by ethnicity, type of undergraduate degree programme, entry status, aptitude at entry, subsequent academic performance and whether secondary educated in the UK or not.
Identifying factors associated with junior doctors’ decisions to follow particular career pathways is particularly important in light of the growing demand-supply imbalance in the number of GPs available to meet service need in primary care. The fact that females are more likely to apply to GP may mitigate against low numbers of applicants since there are increasing percentages of female graduates [
31]. However, numbers of applicants to GP training are not increasing proportionately with feminisation of the workforce since attitudes, values and experience during GP placements are also contributory factors [
32]. The widening participation agenda aims to balance the characteristics of the workforce with the patient population it serves, in order to provide the best possible care [
33,
34]. Our findings confirm that this agenda should take account of ethnicity, sex, social status and type of secondary school education when developing selection strategies to widen participation in medicine. Using aptitude tests, such as UKCAT, to set thresholds for undergraduate selection may help to open opportunities for under-represented sociodemographic groups [
35], but cut scores should not be set so high that candidates selected are more likely to pursue non-GP specialties, since we found an inverse relationship between UKCAT score and likelihood of applying to GP training. Workforce planning strategies should take account of educational factors that affect trainees’ career choices to inform policies for expanding medical student numbers and postgraduate training posts. The UK is increasing medical school numbers by an extra 1500 places in order to improve future service provision [
36]; these resources should be targeted at schools that deliver graduates likely to fill significant gaps in the workforce. The Association of American Medical Colleges has responded to projected shortfalls by increasing enrolment of medical students by 30% and expanding graduate medical education programmes [
3]. However, our study offers no evidence to support the latter strategy; we found that, among doctors who had entered medical school as graduates, those from graduate entry programmes were no more likely than those from standard programmes to apply for GP training.
Understanding the interactions involved in complex career decision-making processes requires mixed methodological, longitudinal studies of large student cohorts. National datasets such as UKMED provide opportunities to analyse a multitude of factors associated with applications to specific medical careers, successful appointment and subsequent completion of specialty training. We have found significant independent factors associated with applications to GP specialty training related to personal characteristics, educational environment and academic attainment during training. Career choice is a dynamic process; future research should explore how these decisions are affected by individual motives and values, as well as the changing learning environments in which they are made.