Puddey et al. BMC Medical Education (2015):74
DOI 10.1186/s12909-015-0359-5
RESEARCH ARTICLE
Open Access
Medical student selection criteria and
socio-demographic factors as predictors of
ultimately working rurally after graduation
Ian B Puddey1*, Annette Mercer1, Denese E Playford2 and Geoffrey J Riley2
Abstract
Background: We have previously demonstrated that both coming from a rural background and spending a
year-long clinical rotation in our Rural Clinical School (RCS) have independent and additive effects to increase
the likelihood of medical students practicing rurally following graduation. The current study assesses the extent
to which medical school selection criteria and/or the socio-demographic profile of medical students may further
facilitate or hamper the selection of students ultimately destined for the rural medical workforce.
Methods: The study comprised 729 students, admitted from secondary school since 1999 and having graduated
by 2011, whose actual workplace location in 2014 was classified as either urban or rural using the Australian
Health Practitioner Regulation Agency database. Selection factors on entry (score from a standardised interview,
percentile scores for the 3 components of the Undergraduate Medicine and Health Sciences Admission Test
(UMAT) and prior academic performance as assessed by the Australian Tertiary Admissions Rank) together with
socio-demographic factors (age, gender, decile for the Index of Relative Socioeconomic Advantage and Disadvantage
(IRSAD)), were examined in relation to ultimate rural destination of practice.
Results: In logistic regression, those practicing in a rural location in 2014 were more likely to have come from the
lower 6 IRSAD deciles (OR 2.75, 95% CI 1.44, 5.23, P = 0.002), to be older (OR 1.86, 95% CI 1.09, 3.18, p = 0.023) and to
have a lower UMAT-3 (Non-verbal communication) score (OR 0.98, 95% CI 0.97, 0.99, P = 0.005). After further controlling
for either rural background or RCS participation, only age and UMAT-3 remained as independent predictors of current
rural practice.
Conclusions: In terms of the socio-demographic profiles of those selected for medical school entry from secondary
school, only older age weakly augmented the selection of graduates likely to ultimately work in a rural destination.
Among the selection factors, having achieved higher scores in UMAT-3 tended to mitigate this outcome. The major
focus in attempts to grow the rural medical workforce should therefore remain on recruiting medical students from a
rural background together with providing maximal opportunity for prolonged immersion in rural clinical environments
during their training.
Keywords: Medical school, Selection criteria, Rural medical workforce
* Correspondence: Ian.Puddey@uwa.edu.au
1
Faculty Office, Faculty of Medicine, Dentistry and Health Sciences, University
of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia
Full list of author information is available at the end of the article
© 2015 Puddey et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain
Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,
unless otherwise stated.
Puddey et al. BMC Medical Education (2015):74
Background
The state of Western Australia, which occupies a land
mass that constitutes approximately a third of the area
of Australia and with approximately one tenth of Australia’s
population, is encountering increasing difficulties in the
provision of adequate health and medical services to what
are widely dispersed rural communities. An ongoing major
challenge has been to recruit sufficient medical practitioners to service these rural and remote regions. In response to this challenge, and on the basis of mounting
evidence that a rural background leads to more students
opting for a career in rural practice [1,2], the University of
Western Australia (UWA) began a Rural Student Recruitment program [3]. In addition in 2002, on the premise that
increased opportunities for student immersion in rural clinical environments would also hopefully enhance the prospects of its graduates choosing to practice rurally after
graduation [4], UWA commenced a Rural Clinical School
(RCS). In a recent report [5] we have demonstrated that
these strategies appear to be achieving the desired effect
with students from a rural background 7.5–fold more likely
to be practicing in a rural setting than their urban counterparts. Furthermore, students from both urban and rural
backgrounds who spend the fifth year of medical school in
clinical rotations in our RCS, are approximately 4–fold
more likely to be in rural practice 3 or more years after
completing their MBBS degrees [5].
A relatively under-explored question, however, is the
extent to which other factors may further enhance the
chances of medical students choosing to work rurally
after graduation. In this respect, we have previously
assessed a range of socio-demographic factors together
with the selection factors used for medical school entry
that might dictate future intentions of newly enrolled
medical students to ultimately pursue rural practice
[6]. In that study we analysed a cohort of students admitted from 2006 to 2011 who had completed the
Medical Students Outcomes Database Questionnaire
[7] and answered a question on their intent after
graduation to practice in a rural site versus an urban
site. The results confirmed the anticipated strong influences of both a rural background effect as well as
intention towards generalist rather than specialist
practice on intentions to practice rurally. However, the
results also suggested that a focus on increasing the
recruitment of students from lower socio-economic
areas and perhaps setting lower thresholds with respect to the very high Australian Tertiary Admissions
Rank (ATAR) scores required for entry, might be further potential strategies to consider in efforts to bolster
the rural workforce.
We have now had the opportunity to explore this in a
cohort of students who have already graduated from our
medical school and who are now in at least their third
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year in the medical workforce. This has permitted an
analysis of the socio-demographic profile of entering
medical students together with those factors used in
their selection for medical school, as potential predictors of those who ultimately have elected to practice in
a rural site.
Methods
Study cohort
In 1999 MBBS students entering the University of Western
Australia (UWA) Medical School did so via a revised selection process which introduced, in addition to the previous
single entry criterion of academic performance as assessed
by ATAR [8], a score from a structured interview [9] and
the score from an aptitude test - the Undergraduate Medicine and Health Sciences Admission Test (UMAT) [10].
The current study therefore, comprised all standard entrants from secondary school to UWA from 1999 to 2006
who were admitted via the new selection process and who
subsequently graduated by 2011, ensuring each graduate
was in at least their third post-graduate year at the time of
data collection. In February 2014 information was accessed
from the Australian Health Practitioner Regulation Agency
(AHPRA) database to identify each graduate’s current
workplace location. Graduates were designated as working
rurally if their primary practice location was in an area
defined by the Australian Standard Geographical Classification - Remoteness Area (ASGC-RA) [11] as RA 2–5, and
urban if RA 1.
These five geographical areas are based on the Accessibility/Remoteness Index of Australia (ARIA) [11]. ARIA is
a continuous varying index with values ranging from 0
(high accessibility) to 15 (high remoteness), and is based
on road distance measurements from over 12,000 populated localities to the nearest Service Centres in five size
categories based on population size. ASGC-RA 1 refers to
the Major Cities of Australia (ARIA score 0–0.2) characterized by relatively unrestricted accessibility to a wide
range of goods and services and wide opportunities for
social interaction; ASGC-RA 2 refers to Inner Regional
Australia (ARIA score >0.2 and ≤2.4) and is characterized
by some restrictions to accessibility of some goods, services and opportunities for social interaction; ASGC-RA 3
designates Outer Regional Australia (ARIA score >2.4
and ≤5.92) and is defined by significantly restricted accessibility; ASGC-RA 4 defines Remote Australia (ARIA
score >5.92 and ≤ 10.53) with very restricted accessibility
and ASGC-RA 5 defines Very Remote Australia (ARIA
score >10.53) with very little accessibility.
This study is part of a larger ongoing project which
includes the follow-up of all school leaver entrants to the
MBBS course at the University of Western Australia. Results of the larger project have been reported elsewhere [9].
The project has been approved by the Human Research
Puddey et al. BMC Medical Education (2015):74
Ethics Committee at UWA as an amendment to the larger
project (file reference RA/4/1/2178).
Selection factors
A more detailed description of each of the 3 selection
factors (ATAR, standardised interview and UMAT) can
be found in our previous reports [9,12,13]. They were
weighted in a ratio of 1 : 1 : 1 to determine a final composite selection score from which all applicants were
subsequently ranked before selection and final offers of a
medical school place. In the current analysis, the three
component UMAT scores, UMAT-1 (Logical reasoning
and problem solving), UMAT-2 (Understanding people)
and UMAT-3 (Non-verbal reasoning), were independently evaluated together with the total score because of
their different and independent constructs. The UMAT
scale has changed over time and scores are not necessarily comparable between years. Therefore percentile
ranks, which enable a measure of the relative standing of
a candidate within each annual cohort, have been utilised in this study rather than the raw score.
Socio-demographic factors
Age, gender and type of school attended - government
(publicly funded) or independent (fee paying) - were recorded at entry to medical school. Age at graduation
from medical school exhibited marked kurtosis and skew
and so has been dichotomised into those 23 yr and
younger vs those 24 yr and older. As a socioeconomic
indicator, the correspondence postcode at entry for each
student was linked to the Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) score from
the Australian 2006 census Socio-Economic Indices for
Areas (SEIFA) [14]. The construct for SEIFA codes, and
the caveats in relation to their use as socio-economic indicators, have previously been described [12]. A dummy
variable was constructed which dichotomised the cohort
into the top 4 deciles for IRSAD score vs the bottom 6
deciles. Region of origin was determined from country
of origin according to major regional groups as outlined
in the Australian Standard Classification of Countries
for Social Statistics [15]. Given the relatively small numbers of students in some groups they were collapsed into
5 groups for analysis - those from Oceania (Australia,
New Zealand, Papua New Guinea and proximate Pacific
islands), UK and Ireland, NE and SE Asia, Southern Asia
(India, Pakistan, Sri Lanka and Bangladesh) and Other.
Rural background and RCS participation
Up until 2007 all applicants to the UWA medical school
were considered from a rural background if they had
lived in a rural area of Western Australia for a minimum
of two years and, during that period, completed year 12
at a rural secondary school – “rural” being defined as a
Page 3 of 10
distance of >75 kms from the Perth Central Business
District. The utilisation of the ASGC Remoteness Area
classification to define rurality did not commence at
UWA until 2010. However, all subjects within the above
rural definition were from areas designated ASGC-RA 2
to 5 and an ASSGC-RA classification for each student
was based on their rural town of origin in the present
analysis. Otherwise students were classified as urban
background (ASGC-RA 1).
The final cohort was linked to a database of all students
who have been enrolled in the RCS since it commenced in
2002 to identify those who completed level 5 of the MBBS
course in a rural location. The RCS up to 2011 occupied
13 sites, two designated ASGC-RA 2, three designated
ASGC-RA 3, seven designated ASGC-RA 4 and one designated ASGC-RA 5.
Statistics
Data were analysed using IBM SPSS Statistics Release
20.0.0. All values are reported as Mean ± SEM. Univariate comparisons by rural vs urban background were
made using the χ2 test for categorical variables and unpaired T-test for each selection factor. Univariate analysis of predictors of practicing in a rural vs urban
environment utilised logistic regression for categorical
variables and unpaired T-tests for each selection factor.
Multivariate logistic regression models were constructed
for the major outcome variable of current site of practice
in 2014 (urban vs rural), using the full set of selection
factors and socio-demographic variables outlined above.
Further models included adjustment for the independent
and combined influence of both rural background and participation in the RCS on ultimate practice destination.
Results
Summary statistics
There were 774 subjects eligible for the analysis of
whom 31 could not be traced on the AHPRA database,
10 who were either overseas or registered as nonpracticing and 4 who were deferred entrants from 1998
and did not sit the UMAT or interview (Table 1) (final
effective sample size N = 729, participation rate 94%). A
comparison of the selection scores (UMAT and each of
its components, ATAR and interview score) in those
who were finally included versus those who did not, revealed no significant differences in ATAR, the interview
score, total UMAT percentile score or percentile score
in each of its 3 components. There were no significant
differences in the distribution for both groups in terms
of age, rural background, IRSAD decile and type of high
school attended but those not included had a higher
proportion of females (χ2 = 7.0, P = 0.005).
The final cohort had a mean age of 23.6 ± 0.04 yr at completion of the course. Approximately 86% were of urban
Puddey et al. BMC Medical Education (2015):74
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Table 1 Description of University of Western Australia standard pathway entrants from 1999 who graduated by 2011
Year of completion of course
2004
2005
2006
2007
2008
2009
2010
2011
Total
60
81
89
93
106
123
125
97
774
Overseas or Non-practicing
2
4
3
0
0
1
0
0
10
Deferred entry – ATAR alone
2
2
0
0
0
0
0
0
4
Unable to be traced
4
6
1
7
7
3
2
2
31
54
71
85
86
99
120
123
95
729
Graduates
Ineligible for inclusion
Included in the study
origin (N = 630) and 14% of rural origin (N = 99), 55% were
female (N = 404) and 45% were male (N = 325), 63% were
from an independent school background (N = 420) and
37% from a Government school background (N = 243),
12.5% were from the lower 6 IRSAD deciles (N = 91) and
87.5% from the upper 4 deciles (N = 638). This profile was
not significantly different for each year of the cohort from
1999 to 2006.
Selection criteria and socio-demographic factors by rural
vs urban background are listed in Table 2. Rural students
had lower academic entry scores and UMAT percentile
scores but interview scores were not significantly different.
Rural students were older and more likely to come from an
area of reduced socio-economic advantage and increased
socioeconomic disadvantage. Rural students included more
females and were more likely to have attended Government
rather than independent schools and were twice as likely to
have been enrolled in the RCS during the 5th year of the
course.
Univariate analysis of the selection factors
Because rural students were recruited with lower overall
academic entry scores and UMAT scores, univariate analyses of the selection factors for each group were conducted
separately (Tables 3 and 4). In urban background students,
the academic entry score and interview score were no different between those currently working in a rural area vs
those in an urban site of practice. The percentile scores for
UMAT-1 and UMAT-2 were also similar, while for
UMAT-3, scores were lower in those currently in a rural
site (Table 3). For rural background students, academic
entry score, interview and total UMAT percentile score as
well as scores in each of its components were no different
in those currently working rurally vs those in an urban site
of practice (Table 4).
Univariate analysis of the socio-demographic data
Univariate analysis of socio-demographic variables in the
prediction of the likelihood of students working in a rural
site vs an urban environment are outlined in Table 5. Together, being from rural background and having attended
the RCS predicted a nearly 10-fold increase in the odds of
currently practicing rurally compared to those who were
from an urban background and hadn’t attended the RCS.
Being from an urban background but having attended the
RCS predicted a 3.31-fold increase in the odds of currently
practicing rurally, while being from an rural background
but not having attended the RCS predicted a 2.83-fold increase in the odds of currently practicing rurally. For rural
background students alone, increasing rurality, as measured
by ASGC-RA 1–5 for the town of origin at entry to medical
school, did not further increase the likelihood of practicing
rurally (Table 5). There was however, a significant and increasing trend for those from the lower 6 IRSAD deciles to
be currently working rurally compared to those in the
upper 4 deciles. Students from Government versus independent schools had a small increase in the odds of current
rural practice which was of borderline significance only.
Multivariate analyses of selection factors and
socio-demographic data
The final multivariate logistic regression model is outlined in Table 6. Those practicing in a rural location in
2014 were more likely to come from the lower 6 IRSAD
deciles on admission to medical school (OR 2.75, 95%
CI 1.44, 5.23, P = 0.002), to be older (OR 1.86, 95% CI
1.09, 3.18, p = 0.023) and to have a lower UMAT-3
(Non-verbal communication) percentile score (OR 0.98,
95% CI 0.97, 0.99, P = 0.005). In a separate analysis
(Table 7), both rural background and participation in
the Rural Clinical School were first forced into the
model to control for these established influences on
the actual likelihood of working rurally after graduation. Being from an urban background but attending
the RCS was associated with a more than 3-fold increase in the odds of practicing rurally (P < 0.001)
while having a rural background and attending the
RCS was associated with a more than 5-fold increase in
the odds. After controlling for these influences, only
age (P = 0.035) and UMAT-3 percentile score (P = 0.027)
remained as independent predictors of rural practice. The
further entry of country of origin into the model made
no substantial change in these parameter estimates (data
not shown).
Puddey et al. BMC Medical Education (2015):74
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Table 2 Selection criteria and socio-demographic factors by rural vs urban background
Variable
N
Urban background
N
Rural background
P-value (χ2 test)
Academic entry score (ATAR)
630
99.0 ± 0.04
99
97.50 ± 0.18
<0.001
Interview score
630
28.0 ± 0.2
99
26.9 ± 0.6
0.065
Total UMAT Percentile score
630
84.6 ± 0.4
99
75.8 ± 2.0
0.022
UMAT-1 Percentile score
629
79.7 ± 0.7
99
74.5 ± 2.1
0.865
UMAT-2 Percentile score
629
72.8 ± 0.9
99
73.2 ± 2.2
<0.001
UMAT-3 Percentile score
629
77.5 ± 0.8
99
63.4 ± 2.5
0.009
Age at completion
0.022
Up to 23 yr
377
59.8%
48
48.5%
24 yr and older
253
40.2%
51
51.5%
Female
338
53.7%
66
66.7%
Male
292
46.3%
33
33.3%
Sex
0.010
Secondary school
<0.001
Government
189
33.4%
54
55.7%
Independent
377
66.6%
43
44.3%
Deciles 1-2
4
0.6%
3
3.0%
Deciles 3-4
7
1.1%
12
12.1%
Deciles 5-6
29
4.6%
36
36.4%
IRSAD score
<0.001
Deciles 7-8
93
14.8%
19
19.2%
Deciles 9-10
497
78.9%
29
29.3%
621
98.6%
0
0
ASGC-Remoteness area
Major Cities
<0.001
Inner regional
4
0.6%
35
35.4%
Outer regional
4
0.6%
51
51.5%
Remote
1
0.2%
11
11.1%
Very remote
0
0
2
2.0%
397
63.0%
88
88.9%
Country of origin
Oceania
<0.001
UK and Ireland
25
6.3%
1
1.0%
Eastern and SE Asia
116
18.4%
4
4.0%
Southern Asia
40
6.3%
1
1.0%
Other
52
8.3%
5
5.1%
Significant P values are in bold-faced type.
Discussion
It has previously been noted that selection processes
into medicine have been relatively neglected as a line of
investigation for increasing our understanding of potential predictors of taking up a rural career [16]. We have
now assessed whether there is any predictive value in
the 3 selection factors that have been utilised at our
medical school since 1999, ascertaining the degree to
which they might predict current site of practice in all
students who were now within at least 3 years of their
graduation. After controlling for the multiplicative
effects of a prolonged rural undergraduate experience
during clinical training and previous rural background,
no predictive value was seen for either prior academic
performance at secondary school or for score on a standardised interview. Similarly no predictive value was
seen for the total score on an aptitude test – the UMAT
– or 2 of its components, UMAT-1 (Logical reasoning
and problem solving) and UMAT-2 (Understanding
people). In contrast, UMAT-3 (Non-verbal reasoning)
remained an independent, albeit weak, predictor of
current rural practice with an estimate from the
Puddey et al. BMC Medical Education (2015):74
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Table 3 Selection factors by current site of practice in
urban background subjects
Current site N
of practice
Academic entry score (ATAR) Urban
Interview score
Mean ± SEM P value
(Unpaired
T-Test)
587 98.98 ± 0.04
Rural
43
Urban
587 28.0 ± 0.2
Rural
43
Total UMAT Percentile score Urban
0.210
98.80 ± 0.16
0.731
28.3 ± 0.7
587 84.8 ± 0.5
Rural
43
UMAT-1 Percentile score
Urban
586 77.4 ± 2.7
Rural
43
UMAT-2 Percentile score
Urban
586 72.8 ± 0.9
Rural
43
UMAT-3 Percentile score
Urban
586 78.1 ± 0.8
Rural
43
0.068
81.6 ± 1.7
0.366
79.9 ± 0.7
0.757
71.8 ± 3.0
0.046
69.6 ± 4.1
Significant P values are in bold-faced type.
regression modelling that a 10 percentile increment in
the UMAT-3 score would predict a 13% reduction in the
odds of a rurally-based career. We have previously reported that a weaker performance in UMAT-3 is seen in
females compared to males, in older subjects and in
those with a rural background but a stronger performance by those from Asian language backgrounds [12]. In
the current study, the negative predictive effect of a
higher UMAT-3 score was still present after adjustment
for age and gender and region of origin. It was only significant in urban, not rural background students, however, with modelling in these subjects indicating a 20%
reduction in the odds of being in rural practice with
Table 4 Selection factors by current site of practice in
rural background subjects
Current N
site of
practice
Academic entry score (ATAR) Urban
Rural
Interview score
Total UMAT Percentile score
UMAT-1 Percentile score
UMAT-2 Percentile score
UMAT-3 Percentile score
Mean ± SEM P value
(Unpaired
T-Test)
78 97.54 ± 0.20
0.677
21 97.36 ± 0.45
Urban
78 26.9 ± 0.7
Rural
21 27.0 ± 1.1
Urban
78 75.4 ± 2.2
Rural
21 77.4 ± 4.7
Urban
78 74.7 ± 2.4
Rural
21 73.6 ± 4.9
Urban
78 72.5 ± 2.5
Rural
21 76.0 ± 4.4
Urban
78 63.3 ± 2.9
Rural
21 63.7 ± 4.6
0.731
0.677
0.827
0.503
0.955
each 10 percentile increment in the UMAT-3 score.
UMAT-3 is the one component of this aptitude test that
appears most susceptible to enhanced performance after
prior coaching [17-19]. Given the costs of such coaching
it is conceivable that students from areas of social disadvantage or government vs independent schools might
have had less access to such coaching, offering a possible
explanation, at least in part, for our observation. However, lower UMAT-3 percentile scores were still predictive of rural practice even after these considerations were
taken into account.
No difference was seen in academic entry scores between those who were now in a rural practice setting vs
an urban location in either rural or urban background
students. This result is discordant from that in our previous report [6] which ascertained the predictive value
of selection factors and a number of socio-demographic
variables on the intention of entering medical students
to practice rurally. In that study lower academic entry
scores (especially among urban students) were independently associated with a higher likelihood of an
intention to practice rurally. In the current study, all
subjects entering from secondary school from 1999 were
eligible for inclusion if they were now at least in their
3rd year post graduation, while the eligible subjects for
our previous report were all those who entered from
secondary school from 2006 to 2011. A comparison of
the 2 cohorts reveals that although gender mix and socioeconomic background in the 2 cohorts were similar,
participants in the current study were younger, less likely
to have come from a rural background and more likely
to have come from a government school background, all
factors which may have impacted on academic entry
scores as relative predictors of intended or actual rural
practice.
Very few other studies have evaluated the extent to
which commonly utilised approaches for student selection may also impact on the ultimate intended or actual
destination of practice. In a previous study from our
medical school, in an era when selection was based on
academic merit alone, in 2 cohorts of students who
commenced medical school in 1984 and 1989 respectively, admission scores were not found to be predictive
of either rural practice within 4 years of graduation or a
generalist vs specialist career choice [20]. Pearson et al.
[21], in another Australian study, compared graduate
destinations of students from a medical school that used
traditional academic criteria alone for student selection,
to one that utilised an alternative ‘composite entry’ pathway that also included a psychometric test and an interview. The results were suggestive that choosing students
through the ‘composite entry’ pathway resulted in more
who chose family medicine and psychiatry as career destinations, but there was no evidence of an influence on
Puddey et al. BMC Medical Education (2015):74
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Table 5 Univariate predictors of graduates currently working in a rural environment
Number (%) currently in rural site of practice
Odds ratio (Logistic regression)
P
Rural background/RCS
Urban and RCS non-participant
21/467 (4.5%)
1.0
Rural and RCS non-participant
6/51 (11.8%)
2.83 (1.09, 7.38)
0.033
Urban and RCS participant
22/163 (13.5%)
3.31 (1.77, 6.21)
<0.001
Rural and RCS participant
15/48 (31.2%)
9.65 (4.56, 20.46)
<0.001
Up to 23 yr
27/425 (6.4%)
1.0
24 yr and older
37/304 (12.2%)
2.04 (1.22, 3.44)
0.007
Female
37/404 (9.2%)
1.11 (0.66, 1.87)
0.687
Male
27/325 (8.3%)
1.0
Deciles 1-2
2/7 (28.6%)
5.44 (1.02, 29.05)
0.047
Deciles 3-4
4/19 (21.1%)
3.63 (1.15, 11.51)
0.029
Deciles 5-6
12/65 (18.5%)
3.08 (1.51, 6.28)
0.002
Deciles 7-8
10/112 (8.9%)
1.33 (0.64, 2.78)
0.440
Deciles 9-10
36/526 (6.8%)
1.0
Major Cities
42/621 (6.8%)
1.0
Inner regional
9/39 (23.1%)
4.14 (1.84, 9.28)
<0.001
Outer regional
12/55 (21.8%)
3.85 (1.89, 7.84)
<0.001
Remote and Very remote
1/14 (7.1%)
1.06 (0.14, 8.30)
0.955
0.056
Age at completion
Sex
IRSAD score
ASGC-Remoteness area
Secondary school
Government
27/243 (11.1%)
1.69 (0.97, 2.92)
Independent
29/420 (6.9%)
1.0
52/485 (10.7%)
1.0
UK and Ireland
2/26 (7.7%)
3.30 (0.78, 13.94)
0.104
Eastern and SE Asia
6/120 (5.0%)
2.29 (3.10, 17.24)
0.421
Southern Asia
2/41 (4.9%)
1.45 (0.28, 7.40)
0.657
Other
2/57 (3.1%)
1.41 (0.19, 10.45)
0.737
Country of origin
Oceania
Significant P values are in bold-faced type.
ultimate rural versus urban practice location. Rabinowitz
and colleagues [22] followed up 3414 graduates from the
Jefferson Medical College in Philadelphia and looked at
19 variables that might potentially influence likelihood
of becoming a rural primary care physician. These included the scores from an aptitude test - the Medical
College Admission Test, as well as the undergraduate
science grade point average but neither of these selection
factors for these graduate entry students were shown to
have any significant predictive value.
We have previously reported that for entering medical
students at UWA, an intent to practice rurally was independently predicted by IRSAD - a composite score
which includes indicators of both higher socio-economic
disadvantage and reduced socio-economic advantage [6].
In the current study, although IRSAD demonstrated a
similar univariate trend for actual site of practice after
graduation rather than just intent, this was no longer an
independent predictor after rural background and participation in our rural clinical school were entered into
multivariate models. This reflects the observation that
rural students in this cohort were more likely to have
come from areas of reduced socio-economic advantage
and increased socioeconomic disadvantage. It may also
reflect the fact that responses to a question on intended
future site of practice at entry to medical school may have
been dictated at least in part by idealistic motivations. A
decline in idealism as a student progresses through a
Puddey et al. BMC Medical Education (2015):74
Page 8 of 10
Table 6 Multivariate logistic regression with current urban vs rural site of practice as the dependent variable and
selection factors and socio-demographic variables as the predictor variables (N = 728) (Nagelkerke R Square = 0.09)
Predictor variable
B
S.E.
P value
Odds ratio
95% CI for odds ratio
Lower
Upper
1.09
3.18
0.51
1.56
1.44
5.23
Age at completion
23 yr or younger
24 yr or older
1
0.622
0.274
0.023
1.86
Sex
Male
Female
1
−0.119
0.287
0.679
0.89
1.011
0.329
0.002
2.75
IRSAD score
Deciles 7-10
Deciles 1-6
1
ATAR
−0.106
0.104
0.308
0.90
0.73
1.10
Interview score
−0.003
0.024
0.888
1.00
0.95
1.05
UMAT-1 Percentile score
−0.009
0.007
0.225
0.99
0.98
1.01
UMAT-2 Percentile score
−0.004
0.006
0.536
1.00
0.98
1.01
UMAT-3 Percentile score
−0.016
0.006
0.005
0.98
0.97
1.00
Significant P values are in bold-face type.
Table 7 Multivariate logistic regression with current urban vs rural site of practice as the dependent variable and rural
background and RCS participation, selection factors and socio-demographic variables as the predictor variables (N = 728)
(Nagelkerke R Square = 0.149)
Predictor variable
B
S.E.
P value
Odds ratio
95% CI for OR
Lower
Upper
0.48
4.52
Rural background and RCS participation
Urban and no RCS participation
Rural and no RCS participation
1
0.388
0.571
0.497
1.48
Urban and RCS participation
1.213
0.330
<0.001
3.36
1.76
6.42
Rural and RCS participation
1.631
0.487
0.001
5.11
1.97
13.25
1.04
3.10
0.41
1.30
Age at completion
23 yr or younger
24 yr or older
1
0.586
0.278
0.035
−0.322
0.297
0.278
1.80
Sex
Male
Female
1
0.73
IRSAD Score
Deciles 7-10
Deciles 1-6
ATAR
1
0.562
0.390
0.150
1.75
0.82
3.78
−0.093
0.117
0.429
0.91
0.72
1.15
Interview score
−0.011
0.025
0.662
0.99
0.94
1.04
UMAT-1 Percentile score
−0.008
0.008
0.261
0.99
0.98
1.01
UMAT-2 Percentile score
−0.001
0.007
0.856
1.00
0.99
1.01
UMAT-3 Percentile score
−0.013
0.006
0.027
0.99
0.98
1.00
Significant P values are in bold-face type.
Puddey et al. BMC Medical Education (2015):74
medical course has been well documented [23,24] and
may have been another factor to consider in explaining
differences in our 2 study outcomes, with initial intentions to practice in under-served and disadvantaged rural
communities subsumed by motivations that become increasingly influenced by social prestige in relation to career choice and specialisation, increasing student debt and
desire for a higher income or better job security. In the
previously discussed Jefferson Medical College study [22],
the only socio-economic variables evaluated were maternal and paternal education, anticipated income bracket
on graduation and anticipated proportion of low income patients in their future practice and none of these
independently predicted ultimately practicing in primary rural care.
In the present study there was no gender difference in
terms of those in rural practice after graduation. In
2008, in the Medicine in Australia: Balancing Employment and Life (MABEL) study, a prospective cohort of
over 10,000 Australian doctors was studied in relation to
type and location of practice [25] and males were found
to have nearly twice the odds of females to be practicing
in rural areas. Other studies have also indicated that
men rather than women are more likely to enter rural
practice [26] although a previous systematic review of
determinants of choosing to practice rurally, indicated
that this has not been a consistent finding [2]. Following
changes to our selection processes in 1999 [13], the increase through subsequent cohorts of medical students
in the proportion of females (including numbers of females from a rural background), is likely to have been a
factor in our neutral result.
Page 9 of 10
school selection factors or student socio-demographic
profiles on the likelihood of ultimately taking up rural
practice after graduation. Among the 3 selection factors
utilised for entry into our medical school from secondary school (ATAR, interview and UMAT), only a lower
percentile score in UMAT-3 weakly predicted a current
rural vs urban site of practice. In terms of the sociodemographic profiles of those selected, only older age
weakly augmented the selection of graduates likely to
ultimately work in a rural destination. These results indicate that the major focus of medical school initiatives to
grow the rural medical workforce should remain on
recruiting medical students from a rural background together with providing maximal opportunity for prolonged
clinical clerkships in rural environments for as many students as possible during their training.
Competing interests
The authors have undertaken this study in the course of their employment,
with no funding from any other source, and have no conflict of interest to
declare.
Authors’ contributions
IP contributed to the conception and design of the study, acquisition,
analysis and interpretation of the data and the initial drafting and final
revision of the manuscript. AM contributed to the conception and design of
the study, interpretation of the data and final revision of the manuscript for
important intellectual content. DP contributed to the conception and design
of the study, acquisition and interpretation of the data and the final revision
of the manuscript. GR contributed to the conception and design of the
study and final revision of the manuscript for important intellectual content.
All authors read and approved the final manuscript.
Acknowledgements
We are grateful to Ms Sue Pougnault and Ms Rhonda Worthington for their
careful curation of the rural student recruitment and rural clinical school
databases, respectively.
Limitations of the study
The relatively small overall numbers, the different sizes
of each annual intake as well as slight variation each year
in UMAT scores, interview scores and ATAR for each
cohort are limitations in this study. The use of the
AHPRA data-base to assess current practice location
may have failed to capture shorter periods of rural practice either before or during 2014 and probably underestimates ultimate rural practice destination. The use of
an individual’s postcode as a surrogate for socioeconomic status imputes an index based on the level of
socio-economic disadvantage for all people living in a
defined area and may not be truly reflective of socioeconomic status for each individual [14].
Conclusion
Once the influence of a prolonged immersion in a rural
clinical environment during undergraduate clinical training as well as the effect of coming from a rural background were considered, we were able to demonstrate
very little in the way of a further influence of medical
Author details
1
Faculty Office, Faculty of Medicine, Dentistry and Health Sciences, University
of Western Australia, 35 Stirling Hwy, Crawley, WA 6009, Australia. 2School of
Primary, Aboriginal and Rural Health Care, Faculty of Medicine, Dentistry and
Health Sciences, University of Western Australia, 35 Stirling Hwy, Crawley, WA
6009, Australia.
Received: 15 October 2014 Accepted: 30 March 2015
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