performance issues The interaction of socio-economic status and gender in widening participation in medicine Barbara Griffin1 & Wendy Hu2

CONTEXT The lack of representation of people from low socio-economic and socio-educational backgrounds in the medical profession is of growing concern and yet research investigating the problem typically studies recruitment and selection in isolation. This study examines the impacts of home and school socio-economic status (SES) from application to selection in an undergraduate medical degree. Socio-cognitive career theory and stereotype bias are used to explain why those from backgrounds of low SES may be disadvantaged, especially if they are female. METHODS Home and high school SES information for 2955 applicants and 202 medical students at one Australian medical school was related to application rates and performance on three selection tests (high school matriculation, the Undergraduate Medical and Health Sciences Admissions Test [UMAT] cognitive ability test, a multiple mini-interview) and academic performance in medical school. Interactions between gender and SES were assessed using moderated regression analyses.

RESULTS Applicants from backgrounds of low SES were under-represented. They were further disadvantaged at selection by the use of high school matriculation and cognitive ability tests, but not by the interview. They did not perform more poorly in medical school. Although females applied in greater numbers, a significant interaction between SES and gender indicated that female applicants of low SES were the most disadvantaged by the use of cognitive ability testing at selection. A targeted allowance of applicants from regions of low SES overcame this adverse impact to some extent. CONCLUSIONS Efforts to widen participation that focus on recruitment are insufficient when selection tests have adverse impacts on people from backgrounds of low SES. The addressing of low self-efficacy that arises from socio-cultural factors, together with reductions in stereotype threat, may reduce the current disadvantages imposed by SES in the medical profession.

Medical Education 2015: 49: 103–113 doi: 10.1111/medu.12480 Discuss ideas arising from the article at www.mededuc.com discuss.

1 Department of Psychology, Macquarie University, North Ryde, Sydney, NSW, Australia 2 School of Medicine, University of Western Sydney, Sydney, NSW, Australia

Correspondence: Barbara Griffin, Department of Psychology, Macquarie University, C3A 526 Balaclava Road, North Ryde, Sydney, NSW 2109, Australia. Tel: 00 61 2 9850 9012; E-mail: [email protected]

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INTRODUCTION

It is of growing concern that student cohorts in medical schools around the globe only poorly reflect the socio-demographic diversity of their nations.1–4 The difficulties of diversifying the background of members of the medical profession have been extensively examined in terms of gender and ethnic disadvantage,5 but more recently the focus has turned to the adverse impact of low socio-economic status (SES) on attraction to the profession, selection into a degree programme, and performance as a medical student.6 There is no reason why people from backgrounds of low SES should be less able to become good doctors and thus they should have a fair chance of selection; compellingly, there is evidence to suggest that the greater representation of medical practitioners from disadvantaged groups is related to better health care outcomes for members of these groups.7 Efforts to widen the participation of those from backgrounds of lower SES probably require to address both recruitment (i.e. the point at which a person becomes interested in medicine as a career and eventually applies to study medicine) and selection (i.e. the process used by a university or medical school to rank applicants and make decisions about who will be offered a student place),8 but these are typically examined in isolation and in a somewhat atheoretical manner. The present study draws on social cognitive career theory (SCCT)9 and the concept of stereotype threat10 to explain how diversity variables not only affect recruitment and selection into medicine, but may interact in a more complex manner than previously described. Social cognitive career theory9 proposes a model whereby career choice depends on the interaction between person (e.g. gender), social, economic and experiential or learning factors. These, in turn, influence self-efficacy beliefs (‘Can I get into medicine?’) and outcome expectations (‘If I got in, could I cope with the study and manage the demands?’). Thus, SCCT accounts for some of the complex connections between ‘self-directed and externally imposed influences on career behavior’.9 Those from backgrounds of low SES are therefore more likely to compromise their interests and not apply for entry to training in medicine because they judge themselves to be less capable not only because of economic difficulties, but also as a result of limited opportunities, insurmountable barriers relative to capacity, or a non-supportive environment.

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Indeed, even in countries in which conditions might be thought to favour widened participation,11 such as Australia, there remains poor representation of students from backgrounds of low SES across universities more generally12 and in medical schools more specifically,1 despite expansions of higher education, low unemployment rates, government-provided income support, and the provision of interestfree loans to cover tuition fees that are repaid through the taxation system after graduation and only when the borrower’s salary reaches a moderate level. The only investigation to date on the socio-demographic predictors of application to medicine4 found that SES was a significant determinant of choice to study medicine, with those of lower SES more likely to apply to other courses. School pupils’ perceptions about medical school also support the need to address the social and cultural environments in which career interests are formed.6 The social cognitive mechanisms identified by SCCT have been used to explain the limitations of the career paths of women, illustrating that lower selfefficacy combined with certain cultural constraints narrows choices and subsequent performance.9 The obvious change in women’s career options over recent decades is thought to have increased their perceptions of self-efficacy and expectations of the outcome of undertaking a medical degree, which may explain why women are now not only more likely to apply to medicine,4 but the once male-dominated profession13 has seen the entry of increasing numbers of female medical students.14 By contrast with the change in female participation, there has been limited success in changing the enrolment of students of low SES, despite the fact that many of the programmes run by universities to widen the participation of this under-represented group do address issues of self-efficacy.1,15 Questions remain as to whether the problem lies in recruitment or selection or both. Furthermore, it is not known whether the change in the rates of application by and selection of women is limited to those of higher socio-economic or socio-educational status, who are likely to benefit from increased career self-efficacy and outcome expectations because they have more supportive environments with regard to educational advancement and face fewer financial barriers. The SCCT predictors of self-efficacy for career choice and outcome expectations include ethnicity, culture, family influence and learning experiences; in contexts of low SES, these factors potentially operate more strongly for women than

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Interaction of SES and gender in selection to medicine for men. If so, women from backgrounds of low SES may continue to be disadvantaged in comparison with their low SES male counterparts. The growing focus on recruitment from low SES groups is insufficient if the post-application processes used to select students compound the disadvantage. In other words, even if downstream recruitment is improved, the improvement will be of limited value if selection is biased against those with backgrounds of low SES. For many years selection into medicine relied on previous academic achievement for the ranking of applicants, which was justified by a strong body of evidence showing that high school matriculation scores and a high grade point average (GPA) predict successful performance in medical school.16 Unfortunately, measures of past academic performance are subject to bias against applicants of lower SES, whose average scores are generally lower than those of candidates of high SES.17,18 This type of bias has attracted decades of research in the context of employee selection, in which the term ‘adverse impact’ is used when a selection test causes disproportionately lower rates of hiring in one group over another.19 Predominantly focused on racial differences, there is considerable evidence to show that cognitive ability testing results in adverse impact.20 After much debate, there is now agreement that such racial differences are most likely to represent a function of SES and socio-educational advantage, rather than of underlying ability.21–23 Despite this evidence from non-medical selection contexts, one of the major aims of introducing cognitive ability testing into the selection of medical students was to neutralise the negative diversity effect of GPA or high school matriculation scores.24–26 Studies16,27 have shown that these tests (such as the UK Clinical Aptitude Test [UKCAT], the Medical College Admission Test [MCAT], and the Undergraduate Medical and Health Sciences Admissions Test [UMAT]) do appear to have some predictive validity for subsequent academic performance, but evidence as to whether or not they reduce or increase bias in relation to diversity factors such as gender and SES is mixed. Performing well on a test of cognitive ability is related to a number of factors other than actual ability,28 one of which is the presence of a stereotype threat. Stereotype threat10 occurs when people are led to identify with a negative group stereotype in a given situation, which subsequently threatens

their self-identity. Activation of a negative stereotype is thought to induce anxiety, self-consciousness and low performance expectations, all of which reduce effort and actual performance. Stereotype threat appears to be particularly relevant to women and those of low SES who have previously broken through barriers through the application of their higher levels of ability.10 Even if they apply for entry into a medical degree, those from backgrounds of low SES may be given cues that cause them to approach selection testing believing that they do not fit the stereotype of a wealthy, successful doctor29 and so, in addition to having less educational advantage and testing experience, they may underperform in the face of stereotype threat. Test instructions that refer to intellectual ability induce stereotype threat most strongly in low SES groups.30 The use of interviews is one mechanism for reducing the adverse impact of cognitive ability testing.31 Originally incorporated into the medical student selection process in order to assess important non-cognitive abilities, interview scores were shown in a recent study32 to be unrelated to home (residential) SES, although their use did not reduce the adverse impact introduced by cognitive ability testing. However, a second study33 found no differences in the diversity of SES between students selected by academic grades and those selected on non-cognitive attributes. Further research is needed to investigate whether the apparent lack of SES bias in interviews is also relevant for socio-educational advantage and if the effect is equivalent for male and female applicants. This study therefore aimed to examine the influence of SES and gender at recruitment and selection. In particular, we investigated whether the effects of SES at both stages differ between men and women.

METHODS

Participants Data were sourced from two different samples of, respectively, applicants to and graduates of an undergraduate medical degree offered by an Australian university with a new medical school established with the mission to address doctor shortages in an economically disadvantaged outer-metropolitan region. Group 1 consisted of applicants, including all those who applied in 2012 (n = 2955) other than

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B Griffin & W Hu international applicants because the latter undergo a different selection process and ratings of their SES were not equivalent to those of local applicants. The mean  standard deviation (SD) age of the applicants was 18.90  2.91 years (range: 16– 48 years). The majority of applicants (66.3%) were in their final year of high school and a further 24.2%, although not current school-leavers, had not completed any other tertiary education. A limited number of places are available for applicants who have completed a previous university degree and 8.6% of the applicants of 2012 had done so. The pathway for local students is described below. Group 2 consisted of graduates who had graduated from the medical school in 2011 or 2012 (n =202). Their mean  SD age at the time of admission was 19.44  3.31 years (range: 17–45 years), which is comparable with that of the applicant cohort. Measures Socio-economic status variables Because home and high school background influence the decision to study medicine,34 we measured both. Home SES was calculated according to the applicant’s residential postcode at the time of application using the Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD).35 The IRSAD is based on the 2011 Australian population census data and is derived from a composite of information on income, unemployment rates and home ownership. Scores range from 1 (lowest 10% of suburbs) to 10 (top 10% of suburbs). The percentages of the total population of the state of New South Wales (NSW) (where this university is located and in which 70% of applicants resided) living in the different IRSAD areas in 2011 are included in Table 2 for the purposes of comparison. The educational SES of the participant’s high school was measured according to the Index of Community Socio-Educational Advantage (ICSEA).36 Values on the ICSEA range from 500 (representing extreme educational disadvantage) to 1300 (representing great educational advantage), with a median value of 1000 and an SD of 100. ICSEA values are also described in quartiles: Q1 consists of scores of ≤ 800; Q2 includes scores of 801–1000; Q3 includes scores of 1001–1200, and Q3 comprises scores of ≥ 1201. Gender Information on participant gender was provided in the administrative data.

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Selection measures In the majority of undergraduate medical schools across Australia, student selection is based on three test scores. The first of these is the Australian Tertiary Admission Rank (ATAR), which is a percentile score derived from final high school year assessments. The second is the score on the UMAT, which is a timed, multiple-choice test with three sections that measure different aspects of ability. Section 1 (or UMAT 1), labelled ‘Logical Reasoning and Problem Solving’, relates to measures of verbal reasoning.37 UMAT 2, labelled ‘Understanding People’ resembles a situational judgement test and shows some relation to tests of emotional intelligence,27 and UMAT 3, labelled ‘Non-Verbal Reasoning’, requires pattern or sequence matching and relates to tests of abstract and numerical reasoning.27 The third source of performance data is a nine-station multiple miniinterview (MMI), which has been described elsewhere.38 Briefly, the MMI generates ratings on a scale of 1 (= unsatisfactory) to 7 (= outstanding) at every station, which are averaged to generate a total score. Selection process Most of the universities in Australia that offer an undergraduate medical degree use a combination of the UMAT and ATAR to rank applicants in order to shortlist a selection of applicants for interview. The specific process used at the present university, which includes a special allowance for applicants from the local (socio-economically disadvantaged) region is described in Table 1. Performance in medical school Performance in medical school was measured according to a GPA (ranging from 0 to 7) based on examination results across the 5-year degree. Data analysis The relationships between the diversity variables and the selection test scores and performance in medical school were assessed using correlations (for ICSEA and IRSAD) and independent t-tests (for gender). The total effect of the diversity variables on selection test performance and academic performance, and the interaction between gender and SES were examined using regression analyses. The adverse impact of the selection tests was also examined by comparing the total applicant pool

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Interaction of SES and gender in selection to medicine

Table 1

Selection process

Stage 4 Rank on MMI + UMAT Stage 1

Stage 2

Stage 3

(with MMI weighted

Application

ATAR cut-off

Rank on UMAT scores

more strongly)

Candidates apply

Local region cut-off:

Highest ranked (n = 350) invited to MMI, ensuring at least

Highest ranked 100

during final year

93.5 to be eligible for

half from local region even if UMAT scores are lower than

offered a student place

of high school

Stage 3 ranking

those of higher ranked non-local applicants

Local applicants have no

Non-local cut-off: 95.5

advantage at this stage

to be eligible for Stage 3 ranking ATAR = Australian Tertiary Admission Rank; UMAT = Undergraduate Medicine and Health Sciences Admissions Test; MMI = Multiple Mini Interview

with: (i) a group of 350 applicants who would have been offered an interview if ranking had been based on the ATAR alone, and (ii) a group of 350 applicants who would have been offered an interview if ranking had been undertaken using the three UMAT scores (individually and as an overall average score). The top 100 ranked interviewees were compared with the remainder of the interviewees (because approximately 100 student places are available). The research was approved by the University of Western Sydney Human Research Ethics Committee.

RESULTS

gory 10 were female. There was no significant difference in ICSEA values between male and female applicants. ATAR results In the applicant pool, high school academic performance (ATAR score) was significantly correlated with home and school SES (Table 3), but ATAR scores did not differ between male and female applicants (mean ATAR scores: 95.44 and 95.40, respectively; t = 0.19, p = 0.85). Together, home SES, school SES and gender accounted for 9.3% of the variance in ATAR scores, with school SES (ICSEA) the only significant predictor when accounting for all diversity variables (Table 4).

Applicant profile As Table 2 shows, more women (54.0%) than men applied. The mean  SD IRSAD was 7.35  2.82, and 50.5% of applicants came from suburbs in the two highest SES categories (whereas only 32.5% of the NSW population was resident in those areas). The mean  SD ICSEA ranking was 1117.88  75.41; there were no applicants in the lowest quartile and the majority (88.2%) of applicants fell within the third quartile. There was a higher percentage of female applicants than male applicants in all 10 IRSAD categories. For example, 59.3% of applicants from IRSAD category 1 (lowest SES) and 53.1% of those from IRSAD cate-

As reported in Table 2, using ATAR to shortlist for interview reduced the proportion of females relative to the total applicant pool and increased the proportion of those with a higher home and school SES. UMAT results In the applicant pool, the mean UMAT scores and scores on all three individual UMAT sections were significantly correlated with home and school SES (Table 3). Compared with male applicants, female applicants had significantly lower mean UMAT scores (54.73 versus 55.42; t = 2.43, p = 0.015), UMAT 1 scores (52.96 versus 56.26; t = 8.45, p < 0.001), and UMAT 3 scores (58.21 versus 60.65; t = 6.69, p < 0.001), but significantly

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Table 2 Percentages of female applicants and members of each socio-economic status (SES) group in the applicant pool and in various shortlisting options Top 350

Final

NSW

Appli-

based on

Top 350

Top 350

Top 350

Top 350

popu-

cant

UMAT

based on

based on

based on

based on

lation

pool

total

UMAT 1

UMAT 2

UMAT 3

%

%

%

%

%

50.4

54.0

47.4

36.0

68.6

1&2

8.0

10.7

4.3

5.5

5.7

6.9

3&4

16.5

8.9

4.0

5.5

4.9

5.4

5&6

18.8

14.9

11.0

9.8

12.1

10.7

12.7

7&8

24.1

15.0

15.0

13.0

13.5

9.8

14.2

9 & 10

32.5

50.5

65.7

66.3

63.8

67.1

63.3

Female

Top 100

enrolment

MMI

based on

from

ATAR

pool

MMI

applicants

Graduates

%

%

%

%

%

%

43.7

49.9

47.0

56.0

57.3

52.8

5.5

8.8

10.0

12.6

12.7

4.3

6.7

6.0

10.7

16.3

11.6

8.0

12.6

12.0

13.1

16.0

12.6

9.6

59.9

60.0

51.5

49.4

IRSAD*

ICSEA† Q1

0

0

0

0

0

0

0

0

0

0

Q2

9.0

3.5

2.9

6.1

1.4

1.7

3.1

5.1

4.9

2.7

Q3

88.2

92.5

92.5

90.2

94.0

91.3

92.7

89.9

93.1

94.0

Q4

2.9

4.0

4.6

3.7

4.3

7.0

4.3

5.1

2.0

3.3

* IRSAD 1 and 2: two lowest home SES categories † ICSEA Q1 to Q4: quartile 1 (lowest educational SES) to quartile 4 (highest educational SES) NSW = New South Wales; UMAT = Undergraduate Medicine and Health Sciences Admissions Test; ATAR = Australian Tertiary Admission Rank; MMI = multiple mini-interview; IRSAD = Index of Relative Socio-Economic Advantage and Disadvantage; ICSEA = Index of Community Socio-Educational Advantage

higher UMAT 2 scores (53.02 versus 49.35; t = 11.53, p < 0.001). Together, home SES, school SES and gender accounted for 11.90%, 10.90%, 8.95% and 9.85% of the variance in mean UMAT, UMAT 1, UMAT 2 and UMAT 3 scores, respectively. For all UMAT scores, each diversity variable remained a significant predictor when the others were accounted for (Table 4). As Table 2 shows, using UMAT scores to shortlist for interview reduced the percentage of female applicants relative to the total applicant pool (except for the UMAT 2 score, the use of which increased the proportion of female applicants) and increased the percentage of those of higher home and school SES. For example, the use of UMAT 1 scores alone to select the top 350 students for interview would have resulted in the selection of 16.5% of male applicants and 8.0% of female applicants, giving a female : male selection ratio of 0.48. It should be noted that in the USA, ratios of < 0.80 are considered discriminatory.19 By contrast, the use of UMAT 2 data results in a male : female selection ratio of 0.84. A compari-

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son of the selection ratios of the two highest and two lowest IRSAD categories generates a ratio of 0.375. Multiple mini-interview results In those applicants who actually attended an interview, there was no relationship between MMI scores and socio-economic or socio-educational advantage. Female applicants achieved significantly higher MMI scores than male applicants (5.23 versus 4.97; t = 3.66, p < 0.001). Together, home SES, school SES and gender accounted for 3.5% of the variance in MMI scores, and gender was the only significant predictor when all diversity variables were accounted for (Table 4). Performance in medical school Final GPA was not significantly correlated with either IRSAD or ICSEA values (r = 0.02 and r= 0.03, respectively) and although female applicants had a slightly higher GPA than male applicants, the difference was not significant (mean GPA:

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Table 3

Correlations in applicant sample

1

2

3

4

5

6

7

8

9

Age Gender

0.03

IRSAD

0.02

0.01

ICSEA

0.15

0.03

0.44†

ATAR

0.02

0.00

0.15†

0.30†



0.31†

0.47†





UMAT total



0.08*

0.05*

UMAT 1

0.09*



0.15

0.21

0.29

0.39†

0.87†

UMAT 2

0.10*

0.21†

0.18†

0.18†

0.28†

0.67†

0.42†

UMAT 3













0.78

0.57†

0.22†

0.02



0.22†

MMI

0.17



0.18

0.12



0.20

0.24

0.17 0.06

0.29

0.01

0.42

0.12*

0.17

0.05

* p < 0.05; † p < 0.001 Gender: 1 = male; 0 = female IRSAD = Index of Relative Socio-Economic Advantage and Disadvantage; ICSEA = Index of Community Socio-Educational Advantage; ATAR = Australian Tertiary Admission Rank; UMAT = Undergraduate Medicine and Health Sciences Admissions Test; MMI = multiple mini-interview

4.88 and 4.73, respectively; t = 1.29, p = 0.20). Together, the diversity variables did not account for any significant variance in GPA. These results did not change even after controlling for ATAR and UMAT scores.

significant interaction between home or school SES and gender in relation to MMI scores, ATAR, UMAT 1 scores or GPA.

DISCUSSION

Interaction between gender and SES Regression analyses that included interaction terms (gender multiplied by the standardised version of the SES variable) were conducted to assess whether the effects of SES on each of the selection tests and on GPA differed between male and female applicants. There was a significant interaction between gender and home SES when the dependent variable was the overall UMAT score (t = 2.81, p = 0.005), UMAT 2 (t = 2.61, p = 0.009) or UMAT 3 (t = 2.51, p = 0.012). Likewise, the interaction term between gender and school SES was significant in regressions on overall UMAT score (t = 2.23, p = 0.026), UMAT 2 (t = 2.29, p = 0.022), and UMAT 3 (t = 2.43, p = 0.015). Figure 1 illustrates these interactions. Whereas overall mean UMAT and UMAT 3 scores were higher in males than in females, this effect was more pronounced in females from a home background of lower SES, and low SES compounded the effect of gender disadvantage. On UMAT 2, females generally scored higher than males, but this advantage was slighter in females with a home background of low SES. There was no

This study examined the effects of socio-economic and socio-educational background in the contexts of application and selection to an Australian medical school. Social cognitive career theory was used to explain the effects on applicants and stereotype threat was applied to explain potential effects on selection testing. Among the nearly 3000 applicants, the representation of those from disadvantaged home and educational backgrounds was significantly lower and did not reflect the distribution of these groups in the NSW population. When ranked for selection on the basis of cognitive ability testing, the applicants from areas of low SES were further disadvantaged, especially if they were female. This is the first research to demonstrate an interaction between gender and SES at the point of selection. The role of SES in recruitment The effect of SES at the applicant stage replicates data from the UK4 and reinforces the need to increase the participation of groups of lower SES well before application in order to address poten-

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Table 4

Regression analyses using applicant sample (separate analyses for each dependent [outcome] variable listed)

ATAR

B Gender

Mean UMAT

SE 0.07

0.21

t 0.32

UMAT 1

B

SE

t

0.55

0.27

2.04* †

UMAT 2

B

SE

t

B

3.09†

0.38

8.23† †

UMAT 3

SE 3.72

t

0.32

11.82† †

MMI

B

SE

t

B

SE

t

2.27

0.35

6.44†

0.27

0.07

3.68†



Home SES

0.05

0.04

1.05

0.32

0.05

6.08

0.37

0.07

5.04

0.40

0.06

6.39

0.20

0.07

2.82

0.01

0.01

0.91

School SES

0.03

0.00

14.26†

0.03

0.00

13.98†

0.04

0.00

11.96†

0.02

0.00

6.74†

0.04

0.00

13.18†

0.00

0.00

0.12

F-value R2

93.47†

130.50†

118.31†

95.27†

105.77†

4.97†

0.093

0.119

0.109

0.090

0.099

0.035

* p < 0.05; † p < 0.001 Gender: 1 = male; 0 = female ATAR = Australian Tertiary Admission Rank; UMAT = Undergraduate Medicine and Health Sciences Admissions Test; MMI = multiple mini-interview; SE = standard error; SES = socio-economic status; B = unstandardized coefficient

tially discriminatory recruitment outcomes.4 Although the proportion of applicants representing the two lowest IRSAD categories was not dissimilar to the percentage of the general population represented by these categories, only 38.8% of applicants came from the middle six categories, whereas 59.4% of the general population did so. In the Australian context, in which financial pressure is less likely to inhibit university study choices, the current study supports the SCCT-based claim that social and cultural factors beyond economic issues impact career choice. Social cognitive career theory9 describes how environment-imposed barriers result in the biasing of self-efficacy and outcome expectancy beliefs, which limit the pursuit of medical careers by those from backgrounds of low SES. A recent extension of SCCT39 found that perceived social status added to the prediction of learning experiences and self-efficacy. Programmes that aim to increase application should consider efforts to build confidence levels by challenging barriers and socially imposed identity beliefs. Interestingly, the results of the current study did not indicate an interaction between gender and background SES at application: women from every SES category applied in greater numbers than did men. The effect of SES on selection tests The finding that applicants from areas of low SES go on to suffer significant adverse impact in both cognitive ability testing and (to a somewhat lesser extent) the high school matriculation score indicates that recruitment efforts alone are insufficient. This is contrary to prior claims that the

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improving of application rates enhances diversity more powerfully than does the improving of selection fairness.8 The problem of inherent bias in cognitive ability testing is supported in a large body of literature in the non-medical field,20 as well as in recent research on the UMAT,24 the UKCAT17 and the MCAT.40 The apparent lack of SES-related bias in MMI scores confirms the findings of past research32 and suggests that the use of interviews may mitigate the adverse impact of measures of cognitive ability in selection. Research on stereotype threat30 would suggest that test descriptions or instructions highlighting the ‘ability-based’ nature of cognitive testing induce a greater threat (and thus performance decrement) than is likely to be seen in interview scores because ‘ability’ and ‘academic achievement’ are not used in pre-interview briefings. The medical school’s allowance for applicants from local areas of low SES, which prioritised these applicants for interview shortlisting and subsequently weighted MMI scores for the final programme entry ranking, appears to have been successful in overcoming the selection test bias. Importantly, results showing that students from areas of low SES (a different cohort to that of applicants) performed as well in medical school as those from areas of high SES should give confidence that widening participation will not necessarily lower academic standards in graduating doctors. However, it should be noted that applicants shortlisted for interview and members of the enrolled student sample were likely to be more homogeneous because they were ranked on cogni-

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Interaction of SES and gender in selection to medicine 59

Female Male

Mean UMAT

57 55 53 51 49 47 45

59

Low home SES

High home SES

Low home SES

High home SES

Low home SES

High home SES

Female Male

57 UMAT 3

55 53 51 49 47 45

59

Female Male

57 UMAT 2

55 53 51 49 47 45

Figure 1 The interaction between home socio-economic status (SES) and gender on Undergraduate Medical and Health Sciences Admissions Test (UMAT) performance

tive ability and therefore results may be attributable to an artefact of selection. Had the whole applicant pool been interviewed, effects of SES may have emerged. Furthermore, all of the students selected, regardless of their backgrounds, were ranked academically among the top 10% of all Australian high school-leavers, and thus individuals who were unlikely to succeed academically were excluded from the pool. Gender in recruitment and selection As in recent UK findings,4 female candidates applied for entry to this medical degree in greater numbers than did males, and once selected, performed equally as well as male students. Nevertheless, female applicants were disadvantaged in terms

of UMAT testing, which is used by most universities in Australia and New Zealand to select students into undergraduate medical degrees. Results also showed that this disadvantage was stronger for female applicants from backgrounds of low SES (both home and school), which supports the concept of a stereotype threat. That said, female applicants performed better on UMAT 2 (‘Understanding People’) and on the MMI, although this relative advantage was less strong for those from backgrounds of low SES. The gender differences reflect already known differences in ability testing, whereby male candidates outperform females in tests of numerical and spatial ability,41 previously shown to be related to UMAT 1 and UMAT 3.37 By contrast, female candidates score higher than males on tests of emotional intelligence,42 which is related to

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B Griffin & W Hu UMAT 2.37 However, the interaction with SES is a new finding and requires further research. Limitations Although its findings are statistically significant, the current study is limited to data from only one institution. However, results related to the effect of the UMAT in the applicant pool reflect those of Puddey and Mercer,24 who studied all UMAT test takers across Australia (including those applying to other health professions). Our sample may reflect a higher proportion of low SES applicants than at other universities because of the particular mission of this institution and its provision of an allowance for regional applicants with an SES disadvantage. The use of a suburb-based ranking of SES does not account for the range of individual SES within a suburb, which should be considered in future research.

CONCLUSIONS

People with a background of socio-economic and socio-educational disadvantage apply to medical school in significantly lower numbers than their more advantaged peers and, even when they do so, are adversely impacted by the use of cognitive ability testing. This test bias is more pronounced for female candidates of low SES. We suggest that SCCT offers an explanation for the recruitment results, but additional work to test the SCCT model further downstream of the application process is required. Perhaps more importantly, better understanding of how stereotype threat or other factors might cause female candidates of low SES to underperform in the UMAT is required to inform the better use of this cognitive ability test in selection and to avoid the hampering of efforts to widen participation in the medical profession. The present results would suggest that the selection processes at this university are at least partially successful in addressing concerns regarding the diversity of medical graduates.

Contributors: both authors contributed to the development of specific ideas for this research. BG analysed the data and wrote the first draft of the manuscript. WH advised on the specific context and critically revised the manuscript. Both authors approved the final manuscript for submission. Acknowledgements: We acknowledge the help of Ms Charlotte Harrison in coding the SES data. Funding: none.

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Conflicts of interest: none. Both authors have been funded by the UMAT Consortium to undertake research on the Undergraduate Medical and Health Sciences Admissions Test; this research is unrelated to the current project. Ethical approval: this study was approved by the University of Western Sydney Human Research Ethics Committee.

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Received 24 December 2013; editorial comments to author 22 January 2014, 24 February 2014; accepted for publication 4 March 2014

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The interaction of socio-economic status and gender in widening participation in medicine.

The lack of representation of people from low socio-economic and socio-educational backgrounds in the medical profession is of growing concern and yet...
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