untitled

Role of students’ context in predicting

academic performance at a medical

school: a retrospective cohort study

Tamara Thiele,

Daniel Pope,

A Singleton,

D Stanistreet

To cite: Thiele T, Pope D,

Singleton A, et al. Role of

students’ context in

predicting academic

performance at a medical

school: a retrospective cohort

study. BMJ Open 2016;6:

e010169. doi:10.1136/

bmjopen-2015-010169

▸

Prepublication history for

this paper is available online.

To view these files please

visit the journal online

(http://dx.doi.org/10.1136/

bmjopen-2015-010169).

Received 2 October 2015

Revised 11 January 2016

Accepted 28 January 2016

Department of Psychological

Science, University of

Liverpool, Liverpool, UK

Department of Public Health

and Policy, Institute of

Psychology, Health and

Society, Liverpool, UK

Department of Geography

and Planning, University of

Liverpool, Liverpool, UK

Correspondence to

Tamara Thiele;

t.thiele@liverpool.ac.uk

ABSTRACT

Objectives:

This study examines associations

between medical students’ background characteristics

(postcode-based measures of disadvantage, high

school attended, sociodemographic characteristics),

and academic achievement at a Russell Group

University.

Design: Retrospective cohort analysis.

Setting: Applicants accepted at the University of

Liverpool medical school between 2004 and 2006,

finalising their studies between 2010 and 2011.

Participants: 571 students (with an English home

postcode) registered on the full-time Medicine and

Surgery programme, who successfully completed their

medical degree.

Main outcome measures: Final average at year 4 of

the medical programme (represented as a percentage).

Results: Entry grades were positively associated with

final attainment (p<0.001). Students from high-

performing schools entered university with higher

qualifications than students from low-performing

schools (p<0.001), though these differences did not

persist at university. Comprehensive school students

entered university with higher grades than independent

school students (p<0.01), and attained higher averages

at university, though differences were not significant

after controlling for multiple effects. Associations

between school type and achievement differed between

sexes. Females attained higher averages than males at

university. Significant academic differences were

observed between ethnic groups at entry level and

university. Neither of the postcode-based measures of

disadvantage predicted significant differences in

attainment at school or university.

Conclusions: The findings of this study suggest that

educational attainment at school is a good, albeit

imperfect, predictor of academic attainment at medical

school. Most attainment differences observed between

students either decreased or disappeared during

university. Unlike previous studies, independent school

students did not enter university with the highest

grades, but achieved the lowest attainment at

university. Such variations depict how patterns may

differ between subjects and higher-education

institutions. Findings advocate for further evidence to

help guide the implementation of changes in

admissions processes and widen participation at

medical schools fairly.

INTRODUCTION

Pervasive inequalities in participation in

higher education (HE) are greatest in select-

ive and oversubscribed programmes such as

medicine.

1–6

In 2008, of seven socio-

economic groups included in the National

Statistic Socio-economic Classiﬁcation

(NS-SEC), the three most afﬂuent groups

(ie, students with parents in professional

occupations) accounted for 85% of medical

students in the UK.

These differences in

participation are largely associated with the

well-documented gap in educational attain-

ment between students from socioeconomic-

ally disadvantaged backgrounds and more

privileged students.

6–13

Concomitantly, uni-

versity admissions systems in the UK focus

Strengths and limitations of this study

▪ To the best of our knowledge, this is the first

published retrospective cohort study that used

both postcode-based measures of disadvantage

along with educational background and demo-

graphic information to examine differences in

participation and attainment of medical students.

▪ This study included only medical students at the

University of Liverpool (UoL) enabling a more

precise evaluation of the determinants of higher

education (HE) performance and participation

endorsing evidence-based decision-making in

university admissions processes.

▪ The results and patterns observed may not be

generalisable to other HE institutions, and must

be interpreted in the context of the geographical

and demographic population of the UoL.

▪ This study included only students who were suc-

cessfully admitted and completed their medical

degree, thereby restricting the extent to which

findings are representative of all medical

students.

▪ Trends relating to postcode-based measures of

disadvantage (eg, Index of Multiple Deprivation)

are based on aggregate data, and hence, may

not necessarily relate to individuals themselves

but rather to the areas in which they are based.

Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169 1

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predominantly on the academic records of prospective

students, though the extent to which these are represen-

tative of students’ academic potential has been ques-

tioned.

12–15

Consequently, this represents the main entry

barrier for lower socioeconomic status (SES) appli-

cants.

111–14 16–20

Though numerous interventions have aimed to widen

and extend access to under-represented groups in the

UK medical student population, evidence suggests these

have had limited impact.

1–621

The integration of school,

domicile, ‘neighbourhood’ and socioeconomic context-

ual information into the university admission system

more generally has been argued to offer a useful tool to

assist widening participation by situating individual prior

attainment within the context of the circumstances in

which results were obtained.

22–27

The argument follows

that inclusion of contextual data could enable universities

to identify academic potential that may not be reﬂected

in prior attainment alone, and most importantly, assist in

making decisions about students from disadvantaged

backgrounds.

18 19 28–34

However, though previous studies

have examined associations between students’ back-

ground characteristics and academic performance

nationally and for individual universities,

10 17–19 28

there

is a dearth of studies focusing speciﬁcally on medical

students, and considering measures of disadvantage,

alongside relative school performance to identify context-

ual effects on prior academic attainment.

1252734

Ensuring such impacts are understood, and then

managed in an equitable way is critical to medical school

admissions systems engendering greater social responsi-

bility, given that students’ life chances and opportunities

can be impacted by such decisions.

134–39

Arguments for

increasing diversity in medical schools also focus on the

beneﬁts that training in demographically heterogeneous

populations has on doctors’ understanding of others

sociocultural backgrounds, which can improve the quality

of medical care they provide.

21 32

Postadmission, it is also

of great importance, that medical schools can identify

and provide appropriate support structures for students

with academic potential to perform well in their studies,

and assist those that may require additional support.

237–

Given that differences have been identiﬁed in the

sociodemographic composition of students even

between elite universities, recognising these differences

and exploring how trends in academic performance

may vary, is important.

11 18 31 35 40–42

The present study

at the University of Liverpool (UoL) investigates the

extent to which students’ contextual background inﬂu-

ences academic perform ance in medical studies.

METHODS

Study context

This study uses data from the UoL, one of the six ori-

ginal ‘red brick’ civic universities and a founding

member of the Russell Group. Traditionally, such elite

universities in the UK have tended to have a greater pro-

portion of students from more afﬂuent backgrounds,

and have higher entry requirements.

30 40 42 43

This,

coupled with the fact that medicine is among the most

competitive and selective programmes, with the highest

entry requirements, is known to affect the composition

of students.

24344

Despite this, the university campus is

based in Liverpool; a city with some of the most socio-

economically depri ved areas in the UK, and has trad-

itionally attracted a high proportion of applicants from

lower SES backgrounds relative to the Russell Group

average.

Data

Ethical approval was requested and granted by the UoL

ethics committee. Data for the study were then obtained

from the UoL central student database. This includes all

necessary student background information, and tracks

performance from the point of application through to

graduation. The study focused on students entering the

UoL between 2004/2005 and 2006/2007. This was the

most recent entry year that allowed analysis of both

entry and exit points. There were no signiﬁcant changes

to the university’s admission policies or grading criteria

during this time period, so data were stratiﬁed by year of

entry, but also treated as a single data set. The data set

contained sociodemographic (sex, age, ethnicity, disabil-

ity, domicile), school attended, prior attainment, and

HE performance information for 571 students. The full

list of variables included in the analysis is described in

table 1.

The 5-year Bachelor of Medicine and Bachelor of

Surgery programme has an annual intake of approxi-

mately 280 students. However, speciﬁc exclusion criteria

were applied that reduced the number of students

included in the analyses. First, only data for students who

successfully completed the full-time 5-year medical

degree programmes were included in this study. Second,

students’ permanent home addresses/postcodes were

used to generate the two area-based measures of disad-

vantage depicted in table 1: Participation of Local Areas

(POLAR 3) and the Index of Multiple Deprivation

(IMD). Students provide their home address/postcode,

during the Universities and Colleges Admissions Service

(UCAS) application process (usually this is their parents’

home address). Correspondence from universities and

UCAS is typically sent to students’ home address. Owing

to the use of students’ home postcodes rather than term-

time postcodes, and the fact that the IMD is generated

separately for each of the UK administrations, students

from outside of England were excluded from analyses.

Data analysis

Given that the ﬁnal year of the medical programme at

the UoL is a placement year which students either pass

or fail, the average attainment of students in year 4 was

selected as the main outcome variable that was included

in analyses. Differences were also explored in entr y-level

2 Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169

Open Access

attainment (UCAS tariff points) based on students’ con-

textual background characteristics (socioeconomic

deprivation, residence in low-participation neighbour-

hood, school type, school performance, sex and ethni-

city). Statistical signiﬁcance of associations between the

independent and outcome variables was assessed using

conventional hypothesis testing for categorical (χ

) and

continuous (independent t test) comparisons.

Univariate linear regression was conducted to describe

the association between contextual background

characteristics (socioeconomic deprivation, residence in

low-participation neighbourhood, school type, school

performance, sex, ethnicity and UCAS tariff points) and

academic performance (as a percentage) of medical stu-

dents at year 4. As differences in degree performance

have been observed between men and women in a

number of studies,

univariate linear regression was also

conducted to explore the extent to which trends

between contextual background characteristics and

attainment varied between men and women.

Multivariable linear regression modelling was con-

ducted to identify which factors were independently

associated with academic performance at year 4. No

entry criteria were speciﬁed for selection of factors to go

into the model, as all were judged a priori to be import-

ant for inclusion. All independent variables (socio-

economic deprivation, residence in low-participation

neighbourhood, school type, school performance, sex,

ethnicity and UCAS tariff points) were selected into the

model using forced entry. Possible interactions were

investigated between: school type × sex; school type ×

school performance; school type × sex × school perform-

ance, where sufﬁcient numbers allowed analysis.

All analyses were undertaken using SPSS (V.21).

RESULTS

There was no evidence of statistical collinearity between

the explanatory factors used in the analysis (all associa-

tions were non-signiﬁcant p>0.05).

Students were predominantly white (78.5%) though

there was a high proportion of Asian students (13.1%)

compared with other ethnic minorities. Almost

two-thirds of the students were women (65.61%).

Table 2 presents a descriptive summary of the associa-

tions between each of the contextual background

characteristics and academic performance.

Signiﬁcant differences were observed in the UCAS

tariff points of students where prior attainment had

been obtained from different school types. Students

from schools denominated under the category ‘state

other’ entered university with the lowest UCAS tariff

points (M=335.17; SD=48.30), but achieved the highest

ﬁnal attainment at university (M=74.73, SD=1.93) along

with students from comprehensive schools (M=74.25,

SD=2.43) (p=0.05). Students from independent schools

attained the lowest averages at university (M=73.56,

SD=2.46).

Only 18% of the student population came from the

most deprived quintile of IMD. These students were

admitted into university with the lowest entry grades

(M=335.35 SD=69.89), and achieved slightly lower ﬁnal

grades at university, though these differences were not

statistically signiﬁcant (p>0.05). Similarly, only 8% of stu-

dents came from neighbourhoods with the lowest HE

participation (highest quintile of POLAR 3), and this

indicator did not predict signiﬁcant differences in per-

formance at entry level or by ﬁnal year at university.

Differences in UCAS tariff points between men and

women were not signiﬁcant. However, at university, men

performed slightly, but signiﬁ cantly less well (M=73.76,

SD=2.66) than women (M=74.33, SD=2.30). Finally, with

regard to ethnicity, though there were no signiﬁcant dif-

ferences in students’ UCAS tariff points by the fourth

year at university, signiﬁcant differences were observed

in the attainment of different ethnic groups. These dif-

ferences varied from those identiﬁed at entry level. This

was particularly noticeable in the attainment of Chinese

students. Speciﬁcally, they achieved the lowest averages

at university compared with students from other ethnici-

ties (M=71.80, SD=3.0) despite entering university with

the second highest grades out of all the ethnic groups

(M=351.67, SD=13.37).

Table 3 summarises the results of univariate linear

regression, depicting associations between contextual

background factors in relation to average attainment at

fourth year. A signiﬁcant positive association was found

between UCAS tariff points (school grades) and fourth

year performance. For every unit increase in UCAS tariff

points, a 0.18% increase in ﬁnal year average perform-

ance was observed (B=0.01, p<0.001). Students from

ethnic minorities were more likely to achieve lower

averages than white students, though these differences

were only statistically signiﬁcant for Chinese (M=71.80,

SD=3.0) (B=−2.61, p<0.001) and Asian students

(M=72.97, SD=2.51) (B=−1.44, p<0.001).

Women students attained slightly, but signiﬁcantly,

higher averages (M=74.33, SD=2.31) at university than

their men counterparts (M=73.76, SD=2.66) (B=−

0.57,

p<0.01). A signiﬁcant association between school type

and ﬁnal year performance at university was also identi-

ﬁed. Speciﬁcally, attendance at comprehensive schools

was associated with higher university achievement com-

pared to attendance at independent schools (B= − 0.82,

p<0.001). There was no signiﬁcant difference in attain-

ment between students who came from neighbourhoods

with differing levels of participation in HE (POLAR 3),

or between those students who attended schools with

low/high levels of performance.

Univariate linear regressions revealed signi ﬁcant statis-

tical differences between men and women in associa-

tions between school type, ethnicity, UCAS Tariff Points

and fourth year performance (table 4). UCAS Tariff

Points were more strongly associated with fourth year

achievement for men (B=0.02, p<0.001) than women

(B=0.02, p<0.01). With regard to school type, compared

Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169 3

Open Access

to comprehensive school students, men from independ-

ent schools were more likely to achieve lower averages

(M=73.76, SD=2.66) (B=−1.36, p<0.01). Though women

students from independent schools, on average, had

lower attainment than comprehensive school students,

unlike with men, attendance at independent schools for

women did not predict signiﬁcant differences in attain-

ment at university (M=73.98, SD=2.31) (B=−0.44,

p=0.206). Additionally, men from sixth form colleges,

and not women, were more likely to achieve lower

averages than comprehensive school students, though

this association only approached signiﬁcance (M=73.27,

SD=2.82) (B=1.02, p=0.069). Second, with regard to eth-

nicity, students who classiﬁed themselves as Asian were

signiﬁcantly more likely to achieve lower averages at

fourth year of university, where men perform ed slightly

less well (M=72.01, SD=3.03) (B=−2.11, p<0.001) than

women (M=73.60, SD=2.37) (B=−0.95, p<0.01). By con-

trast, women and not men of Chinese ethnicity were sig-

niﬁcantly more likely to achieve lower averages than

students who classiﬁed themselves as white (M=71.20,

SD=3.13) (B=−3.35, p<0.001).

Table 1 Description of outcome (educational performance) and predictor (contextual factors) variables

Variables Description

Outcome variables

Year 4 performance Students complete final examinations in year 4 of the medical programme

(year 5 is a practical year where students undertake 8 clinical rotations).

Predictor variables

UCAS Tariff Points UCAS Tariff Points are a system used for allocating points to post-GCSE

qualifications in the UK (eg, for A-levels, A=120, B=100, C=80, etc). These

were calculated from students ’ three highest qualifications and used as a

measure of prior achievement for entry to higher education (HE).

School type The type of school students attended for their A-levels were organised into

five categories including: independent schools, state grammar schools,

state comprehensives, sixth form colleges and a category labelled state

other (includes voluntary aided schools, voluntary controlled schools,

technical colleges and adults colleges)

School performance School performance data were used to contextualise prior attainment,

represented by the overall percentage of students gaining 5A*-E or more at

A-levels or their equivalent. Based on this, a binary classification was also

created where ‘high’ performing schools, represented those schools where

82.5% of students and above achieved 5A*-E or more at A-level or their

equivalent. ‘Low’ performing schools were those where less than 82.5% of

students achieved 5A*-E or more at A-level or their equivalent. These

thresholds were assigned based on the national averages reported in

Department for Education (DfE) performance tables†.

‘Neighbourhood’ domicile: higher education

participation rate (POLAR 3)

POLAR 3 data were matched to the Census Area Statistics (CAS) wards to

illustrate the typical HE participation rate within which students were

domiciled. POLAR 3 data is reported as five quintiles ordered from ‘1’

(lowest participation -<20%) to ‘5’ (highest participation >60%). A binary

classification was created to compare performance of students residing in

areas of lowest participation (1 and 2) to others (3,4 and 5). Quintiles 1 and

2 are those areas, which attract additional widening participation funding for

each student domiciled within them‡

Multiple deprivation The Index of Multiple Deprivation (IMD) (2010) was used to identify the

multiple facets of total deprivation. Students’ postcodes were matched to

Lower Layer Super Output Areas (LSOAs), which contain an average of

1500 households. These were then used to append IMD scores provided

that students had a valid English home postcode. There are 32 482 LSOAs

in England. IMD ranks LSOA with 1 as most deprived and 32 482 as least

deprived. For the analyses, ranks were divided into quintiles, where quintile

1 includes the most deprived LSOA and quintile 5 includes the least

deprived.§

Sex/ethnicity Sex was self-reported by students during the university application process.

Ethnicity was also self-reported by students, and based on this, categorised

as one of the following: White, Asian, Black, Chinese, Mixed and Other

†DfE link http://www.education.gov.uk/schools/performance/.

‡HEFCE POLAR 3 link: http://www.hefce.ac.uk/media/hefce/content/pubs/2013/201328/HEFCE_2013_28.pdf.

§IMD link: https://www.gov.uk/government/publications/english-indices-of-deprivation-2010.

4 Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169

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Multivariable linear regression was carried out includ-

ing all the following variables: UCAS Tariff Points, ethni-

city, sex, school type, school performance, deprivation,

neighbourhood participation and fourth year perform-

ance (table 5). When all these variables were included

in the model, UCAS Tariff Points (school grades) and

ethnicity were found to be independently associated

with fourth year performance. UCAS Tariff Points

(school grades) (B=0.01, p<0.001) remained signiﬁcantly

positively associated with fourth year performance.

Ethnicity remained a signiﬁcant predictor of ﬁnal attain-

ment. Speciﬁ cally, on average, Chinese and Asian stu-

dents achieved 3.01% (B=−3.01, p=0.001) and 1.41%

(B=−1.41, p<0.001) less than white students, respectively.

Though school type differences remained, where inde-

pendent school students were more likely to achieve

lower averages compared to students from other school

types, this association was no longer statistically signiﬁ-

cant when all the variables were incorporated into the

model. Similarly, though men performed slightly less

well than women, the association between sex and aca-

demic achievement approached signiﬁcance but was not

statistically signiﬁcant (B=−0.49, p=0.068). However, the

overall model explains only 12% of the variation in the

ﬁnal grade suggesting that other factors, including

chance, must also play a role. None of the interactions

that were investigated achieved statistical signiﬁcance

(p>0.05).

Table 2 Descriptive breakdown of characteristics of study sample for students

Indicator of student performance

Indicator variable UCAS Tariff Points Year 4 average

Variable N (%) Mean SD Mean SD

School type

Independent 110 (20.88) 342.43 27.29 73.56 2.46

Grammar 115 (21.82) 342.11 32.95 73.92 2.58

Comprehensive 163 (30.93) 347.30 18.88 74.25 2.43

Sixth form 105 (19.92) 346.73 23.25 74.31 2.46

State (other) 34 (6.45) 335.17 48.30 74.73 1.93

p<0.01 p=0.052

School performance

High 426 (89.31) 346.82 42.85 74.22 2.52

Low 51 (10.69) 338.05 22.81 73.96 2.20

p=0.040 p=0.404

Deprivation*

1 88 (17.81) 335.24 69.89 73.82 2.57

2 74 (14.98) 339.71 70.45 74.38 1.99

3 76 (15.38) 345.21 69.83 73.93 2.27

4 112 (22.670 342.94 70.03 74.17 2.42

5 144 (29.15) 343.57 70.27 74.24 2.63

p=0.253 p=0.542

POLAR 3†

1 44 (7.72) 335.35 69.81 73.62 3.00

2 65 (11.40) 348.52 70.29 73.92 2.07

3 109 (19.12) 341.37 70.28 74.37 2.64

4 145 (25.44) 343.10 69.89 74.02 2.41

5 207 (36.32) 341.09 70.13 74.27 2.34

p=0.260 p=0.351

Sex

Males 196 (34.39) 339.90 69.90 73.76 2.66

Females 375 (65.61) 343.18 70.19 74.33 2.30

p=0.227 p=0.012

Ethnicity

White 448 (78.46) 341.25 31.35 74.41 2.33

Asian 75 (13.13) 344.66 28.97 72.97 2.52

Black 5 (0.88) 325.00 30.00 74.40 2.34

Chinese 13 (2.28) 351.67 13.37 71.80 3.00

Mixed 23 (4.03) 343.48 25.34 74.07 2.27

Other 7 (1.23) 353.33 10.33 73.42 3.30

p=0.873 p<0.001

*Deprivation defined by quintiles of Index of Multiple Deprivation (1=Most deprived to 5=Least deprived).

†Neighbourhood higher education participation (1=Lowest participation to 5=Highest participation).

Item Missingness (N): School Type 44; School Performance 94; IMD 77; UCAS Tariff Points 21.

Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169 5

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Table 4 Linear regression between contextual variables and fourth year performance divided by sex

Males Females

Variable x

SD B 95% CI Sig x

SD B 95% CI Sig

School type

State comprehensive (ref) 74.07 2.31 74.37 2.51

Sixth form college 73.27 2.82 −1.02 −2.12 to 0.08 0.069 74.74 2.17 0.31 −0.35 to 0.97 0.357

State other 74.54 1.41 0.26 −1.79 to 2.30 0.806 74.77 2.06 0.34 −0.61 to 1.30 0.481

State grammar 73.91 3.04 −0.38 −1.44 to 0.69 0.485 73.93 2.38 −0.50 −1.14 to 0.14 0.127

Independent school 73.76 2.66 −1.36 −2.33 to 0.38 0.007 73.98 2.31 −0.44 −1.13 to 0.25 0.206

Ethnicity

White (ref) 74.12 2.43 74.56 2.27

Black 73.89 3.14 −0.23 −3.84 to 3.37 0.898 74.74 1.89 0.18 −2.39 to 2.75 0.890

Asian 72.01 3.03 −2.11 −3.13 to 1.10 <0.001 73.60 2.37 −0.95 −1.66 to 0.24 <0.01

Chinese 72.50 2.96 −1.62 −3.73 to 0.485 0.130 71.20 3.13 −3.35 −5.05 to 1.66 <0.001

Other 74.48 2.66 0.36 −1.48 to 2.18 0.700 73.71 2.35 −0.85 −1.83 to 0.132 0.090

Continuous variables

School performance* 0.03 −0.04 to 0.09 0.381 0.001 −0.03 to 0.3 0.961

Socioecomomic status (Index of

Multiple Deprivation; percentile†)

0.002 −0.01 to 0.02 0.804 0.01 −0.003 to 0.01 0.173

Polar 3‡ 0.15 −0.14 to 0.45 0.309 0.07 −0.12 to 0.25 0.491

UCAS Tariff Points 0.02 0.005 to 0.03 <0.001 0.01 0.003 to 0.02 <0.01

*School performance-based on the percentage of students achieving 3 A-levels or equivalent.

†Defined by percentiles of Index of Multiple Deprivation (1=Most deprived to =100 Least deprived).

‡Neighbourhood higher education participation (1=Lowest Participation to 5=Highest Participation).

Table 3 Linear regression between contextual variables and fourth year performance

Variable x

SD B 95% CI Sig

School type

State comprehensive (reference) 74.25 2.43

Sixth form college 74.31 2.46 −0.07 −0.64 to 0.50 0.814

State other 74.72 1.92 0.35 −0.53 to 1.24 0.435

State grammar 73.92 2.58 −0.45 −1.01 to 0.11 0.112

Independent school 73.56 2.46 −0.82 −1.38 to 0.25 <0.001

Ethnicity

White (reference) 74.41 2.33

Black 74.40 2.34 −0.012 −2.12 to 2.09 0.991

Asian 72.97 2.51 −1.44 −2.03 to 0.861 <0.001

Chinese 71.80 3.00 −2.61 −4.70 to 1.31 <0.001

Other 73.92 2.50 −0.50 −1.38 to 0.387 0.271

Sex

Female (reference) 74.33 2.31

Male 73.76 2.66 −0.57 −0.99 to 0.15 <0.01

Continuous variables

School performance* 0.005 −0.16 to 0.01 0.404

Socioeconomic status

(Index of Multiple Deprivation; percentile†)

0.004 −0.003 to 0.011 0.280

UCAS Tariff Points 0.01 0.01 to 0.02 <0.001

Polar 3‡ 0.06 −0.05 to 0.27 0.185

Model parameters (for UCAS Tariff Points)

B0 69.38

R 0.18

0.03

*School performance-based on the percentage of students achieving 3 A-levels or equivalent.

†Defined by percentiles of Index of Multiple Deprivation (1=Most deprived to =100 Least deprived).

‡ Neighbourhood higher education participation (1=Lowest Participation to 5=Highest Participation).

6 Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169

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DISCUSSION

While the use of contextual data in admissions is pro-

moted and considered a powerful tool which medical

schools can use to widen participation,

there is a

paucity of research focusing speciﬁcally on medical stu-

dents, and considering measures of disadvantage, along-

side educational background characteristics to identify

contextual effects on academic attainment.

27 31

The

principal aim of this research was to explore these asso-

ciations, as this has not previously been investigated

using both area-based measures of disadvantage and

school background information within a medical school

environment.

Principal findings from results

A crucial part of this analysis explored the extent to

which school grades in isolation are representative of

‘true academic potential’ by comparing group differ-

ences in attainment at school compared to university.

Consistent with other studies, school grade s (UCAS

Tariff Points) were found to be a strong and signiﬁcant

predictor of academic performance.

11–14 46–51

Statistically signiﬁcant associations were also observed

between three of the contextual background character-

istics and students’ school grades, including school type,

average school performance and ethnicity. Though

school grades were the strongest predictor of university

attainment, school type, ethnicity and sex also predicted

statistically signiﬁcant differences, albeit with some dif-

ferences to those observed when students entered

university.

Compared to students from comprehensive

schools, students from independent schools achieved

lower averages at fourth year, though this association was

not signiﬁcant after controlling for multiple effects.

This association was similar for men and women, but

statistically signiﬁcant only for men. Ethnic differences

in academic attainment evidenced at entry level, differed

from the associations obser ved between these variables

by the fourth year of university. Overall, students who

classiﬁed themselves as white were more likely to achieve

a higher average at fourth year than students of other

ethnicities, though they did not enter university with the

highest grades. These associations also varied slightly

between men and women. With regard to sex, there

were no statistically signiﬁcant differences in the entry

grades of men and women. However, by fourth year at

university, men students performed signiﬁcantly less well

than women students. Socioeconomic deprivation

(IMD), and coming from neighbourhoods with low or

high levels of participation in HE (POLAR 3), did not

predict signiﬁcant differences in ﬁnal year performance.

Table 5 Multiple linear regression including all contextual variables and fourth year performance

Variable x

SD B 95% CI Sig

School type

State comprehensive (reference) 74.25 2.43

Sixth form college 74.31 2.46 −0.12 −0.82 to 0.57 0.727

State other 74.72 1.92 0.67 −0.72 to 1.92 0.370

State grammar 73.92 2.58 −0.22 −0.98 to 0.54 0.566

Independent school 73.56 2.46 −0.29 −0.99 to 0.42 0.426

Ethnicity

White (reference) 74.41 2.33

Black 74.40 2.34 −2.51 −5.77 to 0.75 0.131

Asian 72.97 2.51 −1.41 −2.11 to 0.72 <0.001

Chinese 71.80 3.00 −3.01 −4.70 to 1.31 0.001

Other 73.92 2.50 −0.56 −1.58 to 0.47 0.288

Sex

Female (reference) 74.33 2.31 −0.49 −1.02 to 0.04 0.068

Male 73.76 2.66

Continuous variables

School performance* 0.01 −0.010 to 0.02 0.486

Socioeconomic status

(Index of Multiple Deprivation; percentile)

0.003 −0.01 to 0.01 0.458

Polar 3† 0.05 −0.15 to 0.24 0.634

UCAS Tariff Points 0.01 0.003 to 0.02 0.010

Model parameters

B0 70.14

R 0.35

0.12

*School performance-Based on the percentage of students achieving 3 A-levels or equivalent.

†Neighbourhood higher education participation (1=Lowest Participation to 5=Highest Participation).

‡Defined by percentiles of Index of Multiple Deprivation (1=Most deprived to =100 Least deprived).

IMD link: https://www.gov.uk/government/publications/english-indices-of-deprivation-2010.

Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169 7

Open Access

How do these findings relate to the current evidence

base?

The type of school students attended appears to have a

consistent effect on degree performance.

17–19 52 53

Speciﬁcally, research suggests that for a given set of

A-level results, the degree performance of students who

attended state schools has been found to be higher com-

pared to those who attended private schools, when

other factors were held equal.

13 15 18 28 30

Unlike other

studies,

17–19

students from independent schools did not

enter the UoL with the highest grades. However, consist-

ent with past research, once at university, students from

independent schools achieved lower results than com-

prehensive school students, though these differences

were not signiﬁcant once all variables were incorporated

into the model.

17–19 28

Despite the overlap between

school type and school performance, and the fact that

both have similar beneﬁts in relation to school attain-

ment, results relating to school performance are more

difﬁcult to reconcile with past research, given that ﬁnd-

ings have been more inconsistent.

10 18 19 24 53

That said,

recent studies have found that, conditional on prior

attainment, students from the worst-perform ing schools

were likely to outperform those from the best-

performing schools.

24 30 52 53

Though socioeconomic differences in academic

achievement have been identiﬁed in other

studies,

729465455

they have not been explored using

these speciﬁc measures in published academic research

at other medical schools. It is possible that neither of

the postcode measures of disadvantage (IMD or POLAR

3) predicted signiﬁcant differences in academic achieve-

ment at medical school because less variation exists in

the demographic backgrounds of students admitted to

medical programmes compared with those of other pro-

grammes.

44 56

However, further research is needed to

explore this, as previous studies exploring these effects

have focused largely on students in classiﬁed degree pro-

grammes and used the NS-SEC as a measure of social

class.

18 19 57 58

A number of these studies have identiﬁed

signiﬁcant socioeconomic differences in degree perform-

ance based on NS-SEC data.

18 28

However, various ﬂaws

have been identiﬁed with NS-SEC, which affect the accur-

acy and credibility of ﬁndings derived from studies that

use this measure.

42 57–59

Critically, NS-SEC is derived from

non-mandatory information that is self-declared by indivi-

duals on application to HE making this prone to manipu-

lation and error.

56 57

Additionally, there is evidence that

around 25% of students do not provide this information,

and those who omit this, often ﬁt into target widening

participation (WP) populations.

57 58

For example, Hoare

and Johnston identiﬁed signiﬁcant socioeconomic differ-

ences in attainment between students on classiﬁed

degree programmes based on NS-SEC data, but highlight

the caveat that NS-SEC data was missing for 42% of stu-

dents in their study.

Sex and ethnic differences in educational attainment

have been reported in various studies across different

medical schools in the UK.

56 60–69

Though there were

no signiﬁcant differences in the entry grades of men

and women, consistent with previous research, women

achieved higher averages than men at university.

148516364

Interestingly, associations between variables, speciﬁcally

UCAS Tariff Points, ethnicity, school type and academic

achievement at university, differed between men and

women. UCAS Tariff Points were a slightly stronger pre-

dictor of university achievement for men than women,

even though there were no entry-level differences.

Subgroup differences in school grades, and the extent

to which these predict university performance, have

been identi ﬁed in other studies, and are associated with

institutional and personal factors.

29 30 69–73

Ethnic differ-

ences in attainment have also been associated with these

factors and appear to be widespread.

1 4 29 51 68 69 74

Though students who classify themselves as white have

consistently been found to achieve higher degree out-

comes than students recording other ethnicities, varia-

tions exist with regard to the particular ethnic groups

that perform less well,.

1 4 51 67

In this study, despite

entering with higher grades, students who classiﬁed

themselves as Chinese and Asian performed less well

than students from other ethnic groups. These associa-

tions varied depending on sex. Most notably, only

women and not men who classiﬁed themselves as

Chinese performed signiﬁcantly worse than students

who classiﬁed themselves as white. Though the extent to

which these differences are generalisable is difﬁcult to

discern and requires further exploration, the literature

indicates that these are not local or atypical problems.

Implications of these findings

The present study raises a number of implications for

policymakers and universities that are interested in using

contextual background information to inform their

decision-making processes and admissions policies.

While medical schools have developed complex selec-

tion processes to select the individuals to whom offers

are made, the ability to meet the academic offer is of

crucial importance and represents a principal basis for

selection.

11 14 24 75

This study corroborates previous

research depicting limitations associated wit h school

grades as indicators of future performance and ‘ true

academic potential’.

111

Such evidence has previously

been used to justify the implementation of contextual

data alongside school grades, in university admissions

processes.

18 34 52 76

This may be particularly beneﬁcial

in highly competitive programmes such as medicine,

where a large proportion of applicants achieve top

marks, making it especially difﬁcult to select from

among them.

27 44

However, the uses and importance of

contextual information extend beyond the point of

admissions.

25 57 58

By providing insight into the associa-

tions between contextual background characteristics and

academic attainment, the current analysis also depicts

how contextual information could help identify students

that may require additional support once at university.

8 Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169

Open Access

Additionally, the use of different types of contextual

information in admissions processes is important to tri-

angulate data and ensure that the identiﬁed individuals

are truly from widening participation backgrounds.

Though the use of contextual data in medical

admissions processes is increasingly encouraged, there is

no standardised or universal approach to the use of

contextual data, and very limited guidance on best

practice.

25–27 34 76

As such, there are various questions

and practical issues surrounding the implementation of

policies relating to school type/school-level performance,

including questions of how to ‘equate’ between nations,

how to treat applicants who have changed school, how to

identify able applicants who obtained scholarships to

attend a fee-paying school, and how to ensure that appli-

cants report their educational establishment correctly/

truthfully.

27 52 76

Firm empirical evidence is required to

address these issues and guide institutional policy in

respect of contextual data.

22 23 25 58 76

Limitations and directions for future research

The present study has various limitations that must be

taken into consideration when interpreting ﬁndings.

First, it is important to note that this study included only

students who were successfully admitted and completed

their medical degree. Hence, nothing is known about

students who failed or dropped out, thereby restricting

the extent to which ﬁndings are representative of all

medical students. Additionally, in other studies, interac-

tions have been documented between background

characteristics, educational disadvantage and the likeli-

hood of dropping out of medical school, which could be

explored further.

51 69

Future research should conse-

quently include these students, and explore when and

why students fail and drop out of programmes. Such

information is necessary to ensure that ‘at risk’ students

are successfully identiﬁed and supported. A second limi-

tation of this research is that both the IMD and POLAR

3 are based on aggregate data. Consequently, it should

be noted that trends relating to both IMD and POLAR 3

do not necessarily relate to individuals themselves but

rather to the areas in which they are based. An alterna-

tive approach to IMD/POLAR 3 could be to use

NS-SEC. However, as explained previously, this has lim-

itations, and for the majority of undergraduate admis-

sions, NS-SEC is also not an individual measure, as this

relates to parental occupation.

57 59

Hence, though post-

code measures of disadvantage have weaknesses, there is

less uncertainty attached to these measures, and it is

unlikely that a student would manipulate their postcode,

as they have the imperative that they actually want

contact from UCAS or the university, which is where the

postcodes are sourced. Another limitation of this kind of

research is that it is not possible to control for everything

that affects academic attainment. Some prominent

factors which are likely to affect participation and per-

formance include: personality, motivation, study skills,

family history in HE,

44 64 65 68 71 73 74

parental

occupation, particularly coming from medical fam-

ilies,

477

and intelligence.

Indeed, some variance also

relates to chance and other factors that are unpredict-

able, including life events and illness.

10 12 79

A further potential limitation of the current study is

that information from personal statements and interview

performance were not included in analyses even though

students in the data cohort examined were selected on

the basis of these measures as well as their academic

attainment. Analyses focused on academic attainment,

primarily, due to the weighting this has in the selection

process.

25–27 49–51

Additionally, information from the

personal statements of students in the cohort was highly

limited, as these were marked simply as yes/no to inter-

view. Hence, this did not provide enough information

on which to correlate the quality of a statement with

on-course performance. Data from traditional interviews

was also not included in analyses, as previous studies

have identiﬁed various limitations with these.

80–82

would have been useful to incorporate data from mul-

tiple mini-interviews (MMIs), as these are said to offer

improved reliability and validity over traditional interview

approaches,

and students’ UK Clinical Aptitude Test

(UKCAT) scores which appear to be less sensitive to

background effects compared with school grades.

79 83

However, UoL medical school has only recently changed

its selection process to introduce the use of UKCAT,

MMIs, and alter the use of personal statements. Hence,

though the present study illustrates important differ-

ences between different groups of students at a medical

school in the UK, future studies should explore how the

use of additional criteria (eg, MMIs, UKCAT) in selec-

tion processes affect widening participation and predict

differences between students based on their educational

and sociodemographic backgrounds. Such studies

should take more sophisticated approaches to modelling

by using path analysis or other forms of causal model-

ling, and expand analysis to compare subgroups, and

include other universities.

CONCLUSION

Though there is increasing interest in the use of context-

ual information within university admissions processes,

there is a paradoxical lack of research exploring how

these can be used at medical schools in the

UK.

122273476

The current analyses provide insight into

the associations between contextual background

characteristics and academic attainment. In doing so,

this illustrates how educational attainment at school is a

good, albeit imperfect, predictor of academic attainment

at a medical school. A recommendation from this ana-

lysis is that implementation of contextual data alongside

school attainment during the admissions process could

provide a more detailed and relevant assessment of can-

didates. Furthermore, this could also help to reﬁne the

targeting of students from disadvantaged backgrounds,

and to identify those students who may require

Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169 9

Open Access

additional support once at university.

25 27 29 76

That said,

the patterns observed in the current study differed in

some ways from previous research exploring associations

between contextual background characteristics and aca-

demic attainment. These variations exemp lify how pat-

terns observed nationally may differ between HE

institutions and programmes.

28–30

Further research is

needed to explore these differences at individual

medical schools, and guide institutional policy in respect

of contextual data. This may be key in reducing inequal-

ities perpetuated by current admissions systems, by pro-

moting social mobility and decreasing the

socioeconomic stratiﬁcation of medical schools in the

UK.

Contributors TT, DP, AS and DS were involved in the development of the

research question. TT drafted the manuscript and was responsible for

conducting the data analyses. DP, AS and DS provided guidance at all stages

including: the writing of the present study, data analysis and interpretation of

the data. All the authors approved the final version of the manuscript.

Funding The present study represents part of PhD that is funded by the

University of Liverpool.

Competing interests None declared.

Ethics approval Ethical approval was granted for this study by the University

of Liverpool Ethics Committee board (date of approval: 29/01/13 and the

number/ID of the approval IPHS-1213-LB-039).

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement No additional data are available.

Open Access This is an Open Access article distributed in accordance with

the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license,

which permits others to distribute, remix, adapt, build upon this work non-

commercially, and license their derivative works on different terms, provided

the original work is properly cited and the use is non-commercial. See: http://

creativecommons.org/licenses/by-nc/4.0/

REFERENCES

1. Garlick PB, Brown G. Widening participation in medicine. BMJ

2008;336:1111–13.

2. Mathers J, Parry J. Why are there so few working-class applicants to

medical schools? Learning from the success stories. Med Educ

2009;43:219–28.

3. Powis D, Hamilton J, Gordon J. Are graduate entry programmes the

answer to recruiting and selecting tomorrow’s doctors? Med Educ

2004;38:1147–53.

4. Seyan K, Greenhalgh T, Dorling D. The standardised admission ratio

for measuring widening participation in medical schools: analysis of

UK medical school admissions by ethnicity, socioeconomic status,

and sex. BMJ 2004;328:1545–6.

5. Rolfe IE, Ringland C, Pearson SA. Graduate entry to medical

school? Testing some assumptions. Med Educ 2004;38:778–86.

6. Deakin NA. Access schemes are not enough. BMJ 2011;342:d1941.

7. Feinstein L. Inequality in the early cognitive development of British

children in the 1970 cohort. Economica 2003;70:73–97.

8. Lupton R. Schools in Disadvantaged Areas: Recognizing Context

and Raising Quality (Report Case 76). London: School of

Economics and Political Science: Centre for Analysis of Social

Exclusion, 2004.

9. Leithwood K, Harris A, Strauss T. Reasons for school failure.

Leading school turnaround: how successful leaders transform

low-performing schools. 1st edn. San Francisco, CA: Jossey- Basse,

2010.

10. McManus IC, Dewberry C, Nicholson S, et al. The UKCAT-12 study:

educational attainment, aptitude test performance, demographic and

socio-economic contextual factors as predictors of first year outcome

in a cross-sectional collaborative study of 12 UK medical schools.

BMC Med 2013;11:244.

11. Gorard S. Who is missing from higher education? Camb J Educ

2008;38:421–37.

12. Peers IS, Johnston M. Influence of learning context on the

relationship between A-level attainment and final degree

performance: a meta-analytic review. Br J Educ Psychol

1994;64:1–18.

13. Kirkup C, Wheater R, Morrison J, et al. Use of an Aptitude Test in

University Entrance: a validity study (Report 26). London:

Department for Business, Innovation & Skills, 2011.

14. Chowdry H, Crawford C, Dearden L, et al. Widening participation in

higher education: analysis using linked administrative data. J R Stat

Soc Ser A Stat Soc 2013;176:431–57.

15. Gorard S, Smith E, May H, et al. Review of widening participation

research: addressing the barriers to participation in higher education.

Bristol: HEFCE, 2006. http://www.improvingthestudentexperience.

com/library/UG_documents/Barrier_Gorad_HEA.pdf

16. Palardy GJ. Differential school effects among low, middle, and high

social class composition schools: a multiple group, multilevel latent

growth curve analysis. Sch Eff Sch Improv 2008;19:21–49.

17. Higher Education Funding Council for England. Higher Education

and Beyond Outcomes from Full-Time First-Degree Study (Report:

2013/15). Bristol: HEFCE, 2013.

18. Hoare A, Johnston R. Widening participation through admissions

policy—

a British case study of school and university performance.

Stud High Educ 2011;36:21–41.

19. Smith J, Naylor R. Schooling effects on subsequent university

performance: evidence for the UK university population.

Econ Educ Rev 2005;5:549–62.

20. The Sutton Trust. The missing 3000—State schools

under-represented at leading universities. London: The Sutton Trust,

2004.

21. Mathers J, Sitch A, Marsh JL, et al. Widening access to medical

education for under-represented socioeconomic groups: population

based cross sectional analysis of UK data, 2002-6. BMJ 2011;342:

d918.

22. Cable V, Willetts D. Guidance to the director of fair access. London:

Department for Business, Innovation & Skills, 2011.

23. Croxford L, Raffe D. Differentiation and social segregation of UK

higher education, 1996–2010. Oxf Rev Educ 2013;39:172–92.

24. The Sutton Trust. Comprehensive pupils outperform independent

and grammar pupils in university degrees. London: The Sutton

Trust, 2010.

25. Selecting for Excellence Group. Selecting for Excellence Final

Report. London: Medical Schools Council, 2014.

26. Dowell J, Norbury M, Steven K, et al. Widening access to medicine

may improve general practitioner recruitment in deprived and rural

communities: survey of GP origins and current place of work. BMC

Med Educ 2015;15:165.

27. Cleland J, Dowell J, McLachlan J, et al. Identifying best practice in

the selection of medical student. 2012. Report No.: GMC Res

Report. Pub 12/02/2012.

28. Higher Education Funding Council for England. Schooling effects on

higher education achievement (HEFCE Report: 2003/32). Bristol:

HEFCE, 2003.

29. Office for Fair Access. Trends in young participation by student

background and selectivity of institution (OFFA Report: 2014/01).

London: OFFA, 2014.

30. Thiele T, Singleton A, Pope D, et al. Predicting students’ academic

performance based on school and socio-demographic

characteristics. Stud High Educ 2014;27:1–23.

31. Do PCT, Parry J, Mathers J, et al. Monitoring the widening

participation initiative for access to medical school: are present

measures sufficient? Med Educ 2006;40:750–8.

32. Lemon T, Stone BA. Increasing numbers of medical undergraduates

from lower socioeconomic backgrounds: positive for health care?

Adv Med Educ Pract 2013;4:39–41.

33. Hilton S, Lewis K. Opening doors to medicine. BMJ

2004;328:1508–9.

34. Bridger K, Shaw J, Moore J. Fair admissions to higher education

research to describe the use of contextual data in admissions at a

sample of universities and colleges in the UK. London: Supporting

Professionalism in Admissions, 2012.

35. Tonks DG, Farr M. Widening access and participation in UK higher

education. Int J Educ Management 2003;17:26–36.

36. McHarg J, Mattick K, Knight LV. Why people apply to medical

school: implications for widening participation activities. Med Educ

2007;41:815–21.

37. Greenbank P. The evolution of government policy on widening

participation. High Educ Q 2006;60:141–66.

38. Higher Education Funding Council for England. Outcomes of access

agreement, widening participation strategic statement and National

10 Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169

Open Access

Scholarship Programme monitoring for 2012–13. Bristol: HEFCE,

2014. http://www.offa.org.uk/wp-content/uploads/2014/07/

OFFA201405-1213monitoring-outcomes.pdf

39. Mullen F. Widening Access to HE: Admissions—Contextual Data.

Scottish Parliament Information Centre (SPICe), 2011 [cited 19 June

2014]. Report number: 11/07. http://dera.ioe.ac.uk/2644/

40. Singleton AD. The geodemographics of educational progression and

their implications for widening participation in higher education.

Environ Plan A 2010;42:2560–80.

41. Higher Education Funding Council for England. Schooling effects on

higher education achievement: further analysis—entry at 19 (Report:

2005/09). Bristol: HEFCE, 2005.

42. Singleton AD. Educational opportunity: the geography of access to

higher education. Farnham: Ashgate Publishing, 2010.

43. Reay D, Davies J, David M, et al. Choices of Degree or Degrees of

Choice? Class, Race’ and the Higher Education Choice Process.

Sociology 2001;35:855–74.

44. Gallagher JE, Niven V, Donaldson N, et al. Widening access?

Characteristics of applicants to medical and dental schools,

compared with UCAS. Br Dent J 2009;207:433–45.

45. Hayes AF. Introduction to mediation, moderation, and conditional

process analysis: a regression-based approach. New York: Guilford

Press, 2013.

46. Higher Education Funding Council for England. Causes of

differences in student outcomes (HEFCE report 2015). Bristol:

HEFCE, 2015.

47. McManus IC, Dewberry C, Nicholson S, et al. Construct-level

predictive validity of educational attainment and intellectual aptitude

tests in medical student selection: meta-regression of six UK

longitudinal studies. BMC Med 2013;11:243.

48. McKenzie K, Schweitzer R. Who Succeeds at University? Factors

predicting academic performance in first year Australian university

students. High Educ Res Dev 2001;20:21–33.

49. McManus IC, Smithers E, Partridge P. A-levels and intelligence as

predictors of medical careers in UK doctors: 20 year prospective

study. BMJ 2003;327:139–42.

50. Delaney L, Harmon C, Redmond C. Parental education, grade

attainment and earnings expectations among university students.

Econ Educ Rev 2011;30:1136–52.

51. McManus IC, Woolf K, Dacre J, et al. The Academic Backbone:

longitudinal continuities in educational achievement from secondary

school and medical school to MRCP(UK) and the specialist register

in UK medical students and doctors. BMC Med 2013;11:242.

52. Crawford C. Socio-economic differences in university outcomes in

the UK: drop-out, degree completion and degree class. IFS Working

Paper No. 14/31. 2014. http://www.ifs.org.uk/uploads/publications/

wps/WP201431.pdf

53. Higher Education Funding Council for England. Differences in degree

outcome: key findings (Report: 2014/03). Bristol: HEFCE, 2014.

54. Coley RJ. An uneven start: indicators of inequality in school

readiness. Princeton: Educational Testing Service, 2002.

55. Arulampalam W, Naylor RA, Smith J. Doctor who? Who gets

admission offers in UK medical schools. Bonn: Institute for the Study

of Labor, 2011.

56. Tiffin PA, Dowell JS, McLachlan JC. Widening access to UK medical

education for under-represented socioeconomic groups: modelling

the impact of the UKCAT in the 2009 cohort. BMJ 2012;344:e 1805.

57. Harrison N, Hatt S. Knowing the “unknowns”: investigating the

students whose social class is not known at entry to higher

education. J Furth High Educ 2009;33:347–57.

58. Harrison N, Hatt S. “Disadvantaged Learners”: Who Are We

Targeting? Understanding the Targeting of Widening Participation

Activity in the United Kingdom Using Geo-Demographic Data From

Southwest England. High Educ Q 2010;64:65–88.

59. Rudd E. Students and social class. Stud High Educ

1987;12:99–106.

60. Evans GW. The Environment of Childhood Poverty. Am Psychol

2004;59:77–92.

61. McManus IC, Richards P, Winder BC, et al. Medical school

applicants from ethnic minority groups: identifying if and when they

are disadvantaged. BMJ 1995;310:496–500.

62. Ip H, McManus IC. Increasing diversity among clinicians. BMJ

2008;336:1082–3.

63. Dayiog

lu M, Türüt-Aşik S. Gender Differences in Academic

Performance in a Large Public University in Turkey. High Educ

2007;53:255–77.

64. Sheard M. Hardiness com mitment , gender, and age differentia te

universit y academic performance. Br J Educ Psy chol 2009;79:189–204.

65. Ackerman PL, Kanfer R, Beier ME. Trait complex, cognitive ability,

and domain knowledge predictors of baccalaureate success, STEM

persistence, and gender differences. J Educ Psychol

2013;105:911–27.

66. Richardson JTE. Degree attainment, ethnicity and gender: a

literature review. York: Higher Education Academy, 2010.

67. Richardson JTE. Widening participation without widening attainment:

the case of ethnic minority students. Psychol Teach Rev

2010;16:37–45.

68. Hu S, Wolniak GC. College Student Engagement and Early Career

Earnings: Differences by Gender, Race/Ethnicity, and Academic

Preparation. Rev High Educ 2013;36:211–33.

69. Woolf K, Potts HWW, McManus IC. Ethnicity and academic

performance in UK trained doctors and medical students: systematic

review and meta-analysis. BMJ 2011;342:d901.

70. Aikens NL, Barbarin O. Socioeconomic differences in Reading

trajectories: The contribution of family, neighborhood, and school

contexts. J Educ Psychol 2008;100:235–51.

71. Dubow EF, Boxer P, Huesmann LR. Long-term Effects of Parents

’

Education on Children’s Educational and Occupational Success:

Mediation by Family Interactions, Child Aggression, and Teenage

Aspirations. Merrill-Palmer Q (Wayne State Univ Press)

2009;55:224–49.

72. Bar Haim E, Shavit Y. Expansion and inequality of educational

opportunity: a comparative study. Res Soc Stratif Mobil

2013;31:22–31.

73. Vaz S, Parsons R, Falkmer T, et al . The Impact of Personal

Background and School Contextual Factors on Academic

Competence and Mental Health Functioning across the

Primary-Secondary School Transition. PLoS ONE 2014;9:e89874.

74. Richardson JTE, Woodley A. Another Look at the Role of Age,

Gender and Subject as Predictors of Academic Attainment in Higher

Education. Stud High Educ 2003;28:475–93.

75. Steele F, Vignoles A, Jenkins A. The effect of school resources on

pupil attainment: a multilevel simultaneous equation

modelling approach. J R Stat Soc Ser A Stat Soc 2007;

170:801–24.

76. Bowes L, Jones S, Thomas L, et al. The uses and impact of HEFCE

funding for widening participation. Edge Hill: HEFCE/CFE, 2013.

77. Schildberg-Hoerisch H. Does parental employment affect children’s

educational attainment? Econ Educ Rev 2011;30: 1456–67.

78. Haworth CMA, Davis OSP, Hanscombe KB, et al. Understanding the

science-learning environment: a genetically sensitive approach.

Learn Individ Differ 2013;23:145–50.

79. Tiffin PA, McLachlan JC, Webster L, et al. Comparison of the

sensitivity of the UKCAT and A Levels to

sociodemographic characteristics: a national study. BMC Med

Educ 2014;14:7.

80. Basco WT, Lancaster CJ, Gilbert GE, et al. Medical school

application interview score has limited predictive validity for

performance on a fourth year clinical practice examination.

Adv Health Sci Educ Theory Pract 2008;13:151–62.

81. Benbassat J, Baumal R. Uncertainties in the selection of applicants

for medical school. Adv Health Sci Educ Theory Pract

2007;12:509–21.

82. Cleland J, Patterson F, Dowell J. How can greater consistency in

selection between medical schools be encouraged? A

mixed-methods programme of research that examines and develops

the evidence base. 2014. http://www.medschools.ac.uk/

SiteCollectionDocuments/Selecting-for-Excellence-research-

Professor-Jen-Cleland-et-al.pdf

83. James D, Yates J, Nicholson S. Comparison of A level and UKCAT

performance in students applying to UK medical and dental schools

in 2006: cohort study. BMJ 2010;340:c478.

Thiele T, et al. BMJ Open 2016;6:e010169. doi:10.1136/bmjopen-2015-010169 11

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