How the Medicine and Science programmes can overcome the impacts of low SES secondary school educational disadvantage

ABSTRACT

This study focuses on educational attainment in New Zealand’s undergraduate programmes in medicine and science (N = 750 and N = 4722, respectively), by following four student cohorts over the course of their degree. This research aimed to identify the extent to which studying in competitive academic programmes (Medicine and BSc) attenuate educational disparity related to schools’ socio-economic backgrounds. We found that Science students coming from the lowest Socioe-conomic Status (SES) schools had lower attainment in their first tertiary year but their achievement improved throughout the programme while outperforming all other students in their third year. However, medicine students from lowest SES schools had lower attainment in their first year but from their second year, their attainment was not significantly different from their counterparts coming from higher SES schools. This study’s findings, support the selection processes for these programmes, allowing students from disadvantaged secondary schools to enrol and succeed in competitive academic programmes.

KEYWORDS:

• Equity and diversity
• medical school
• science
• student admission

Introduction

There is plethora of evidence suggesting that socio-economic status (SES) is strongly associated with educational attainment in New Zealand (Maani 2000; Smart 2006) and globally.

For example, in Australia it was found that students coming from Government (publicly funded and managed) schools perform better in the first year in higher education than their counterparts coming from Catholic and Independent schools (Win and Miller 2005). This difference was observed after controlling for the Australian student Tertiary Entrance Rank (TER) (Cardak and Ryan 2009). Moreover, a more recent Australian study found that secondary school fee was negatively correlated with first year university GPA in Science and Allied Health programmes (Anderton 2017). Notably, the impact of studying in secondary schools with low or medium fees on first year university GPA was about 10 times greater than the impact of the Australian Tertiary Admission rank (ATAR) (Table 6; Anderton 2017, 193). Another Australian study found that low SES secondary schools offered fewer technology subjects than higher SES schools (Murphy 2019). A more recent study from New Zealand found that the majority of the inter-ethnic gap in academic performance in higher education was explained by student performance in their first assessment in secondary school (NCEA Level 1, see: Hall 2000).

No direct comparison between the pathways into Medicine vs. pathways into Science has been found in the literature; however, research into pathways to university in general suggests that the SES background impacts on programme choice and enrolment (Leach and Zepke 2005; Shulruf, Hattie, and Tumen 2008b). Recent studies suggest that a school’s SES and the student’s quality of friendship had strong positive impacts on the probability to enrol in university (Hovdhaugen et al. 2021; Vandelannote and Demanet 2021b; 2021a). In addition, although not directly related, it was found that low SES background negatively impacts on medical programmes’ enrolment decisions, made by the applicants and assessors (Eguia et al. 2022; Grant and Kessa Roberts 2022). All these studies suggest that a supportive educational environment has the potential to attract more students from low SES background to study Medicine and Science at the university.

Research from New Zealand, however, suggested that a school’s ability to provide high-quality teaching and educational resources is more important for academic achievement than students’ personal backgrounds (Shulruf, Hattie, and Tumen 2008a; Turner et al. 2010). Nonetheless, the financial resources available to schools and the priorities made by school management did not have a significant impact on student academic outcomes (Tumen 2013). On the other hand, a study in Australia which looked at the impact of school and individual student effects on university outcomes found that a school’s SES had a modest effect on academic performance at the university. Students from low SES schools who enrolled in higher education performed slightly better than their counterparts coming from higher SES schools (Li and Dockery 2015). The authors concluded that ‘the university system appears to level the playing field in terms of academic achievement for students entering from more privileged and less privileged schools’ (Li and Dockery 2015). Thus, it appears that secondary school factors may impact student achievements in higher education. Nonetheless, other views have been observed. Pedrosa et al. (2007) suggested that students from lower SES backgrounds demonstrated ‘educational resilience’ which was described as the ability to transform early life disadvantage into better academic performance in higher education. A New Zealand study published by the Ministry of Education (Scott 2008) suggested that school decile and the size of the school did not have any significant impact on first year degree pass rate at university nor on first year attrition rate, while controlling for various other school and individual characteristics. A similar conclusion was drawn from a sample of veterinary students in Australia where school decile and residential and school postcodes were not associated with academic performance in the programme (Raidal et al. 2019). Overall, it appears that although the literature is inconclusive, secondary school characteristics are likely to have an impact on student academic achievement in higher education. The unanswered question is whether the school socio-economic status (i.e. socio-economic background) has a lasting impact on student achievement, or if the effect is mitigated throughout the academic programme. In this study, we focused on two programmes taught in New Zealand universities: Medicine, taught only in the University of Auckland and Otago University; and Science (BSc), taught in seven out of the eight New Zealand universities (BSc is not being taught in Lincoln University). The overarching objective of this study was to identify the extent to which studying in competitive academic programmes (especially Medicine) attenuates any educational disparity related to school socio-economic backgrounds.

Methods

This study uses linked administrative microdata from the Integrated Data Infrastructure (IDI). The integrated data, held by Statistics New Zealand, provides detailed characteristics of young people and their secondary and tertiary educational enrolment and attainment. Our study focuses on young domestic students who were born between July 1990 and June 1994, completed secondary schooling, and enrolled at a New Zealand university immediately after completion of secondary schooling or a gap year. The study population is restricted to students who applied to Medicine (MBChB) for their second year of tertiary studies and studied in the programme in year two; and students who enrolled into Science (BSc) programme and studied in the programme in year one. The follow up of the students was up to year six in Medicine and up to year three in Science, which are the minimum times for completion in each programme, respectively.

In New Zealand, the school enrolment scheme in the public schools is based on zoning, which means that if a family lives within school zone, the children are guaranteed to get a place at that school. Following the official census, government determines the decile for each school, which is a measure of the extent to which the school’s students live in low socio-economic or poorer communities. This measure is used for school funding purposes to compensate schools that draw students from deprived communities and ultimately reduce educational disparity (deciles 1–10, where decile 1 school has the highest proportion of students from low socio-economic background). In this study this decile measure is grouped into quintiles (secondary school quintile), a simple grouping of the 10 New Zealand secondary school deciles to give a proxy of students SES: quintile 1 equates to deciles 1 and 2, quintile 2 to deciles 3 and 4, etc. Private schools are grouped with decile 10 schools (quintile 5).

The response variable is the academic achievement of students for each year of study and is summarised by the within-programme academic z-score. The tertiary achievement data in the IDI is at the student course level (for details see: Freshwater 2023; Murdock 2022), but does not contain grades and only indicates whether a student passed or failed each course s/he enrolled in. Therefore, because the quality measure of student attainment is not available, the course level difficulty score is derived as an approximately equivalent measure. This difficulty score reflects the difficulty of passing the course requirements successfully for all students enrolled in a course. If a student from the study population passes the course, he or she gains the difficulty score of the course. A student’s overall achievement is then weighted by the number, difficulty, and credit value of the courses they completed each year. A more detailed explanation of this process follows.

The difficulty d of a course is given by the failure rate within student’s course cohort, i.e. the number of students who failed a course divided by the number who enrolled it in a given academic year. After removing courses with a 100% or 0% difficulty, this number is normalised, standardised and added to 5, to give an approximately normally distributed variable with a minimum of 0, maximum of 10, mean of 5 and standard deviation of 1. Courses with 100% difficulty (all failed) are set to 10 and those with 0% difficulty (all passed) are set to 0. Analogous to the typical grade-point-average calculation, for each year enrolled at university of each student, we sum the difficulty of each course passed multiplied by the course’s credit value, and divide this number by 120, the typical full-time credit-load per year. This ensures that students are appropriately compensated for their course workload.

The weighted score is a measure of academic achievement for each year of study for each student in the population, which reflects some qualitative measure of attainment which are standardised across all courses, programmes and year of study. Thus, for convenience the weighted score was then multiplied by 100 and is entitled ‘Standardised Score’ (henceforth: StdS). To include in the data students who dropped out from the programme, their records were held in the dataset in the years after the drop-out but their marks were classified as the lowest possible mark which is StdS = 0. In addition, winsorisation (Nyitrai and Virág 2019) was used to replace extreme values which were more than five standard deviations below or above the mean, that is all StdS < 0 or StdS > 10 were replaced with 0 or 10, respectively.

To investigate differences in academic performance among students from different secondary school quintiles and how their performance changes over time, repeated measures analysis of variance (ANOVA) was performed. This longitudinal method of analysis allows us to see, for each programme, if there is a statistically significant difference in mean StdS between quintiles, and between years. Analysis years are different for Science and Medicine students. For Science students, we look at changes in StdS from years 1 to 3 at university, whereas for Medicine students we look at years 2 to 6. The first year from Medicine student analysis is excluded because this is designated as a ‘selection’ year after which students are officially admitted to the medical programme if they perform well enough. Type 3 tests of the fixed effects are conducted, starting with the interaction between quintile and year.

Ethics

This study was exempted from ethical review by the Health and Disability Ethics Committees, Ministry of Health, New Zealand (Letter from Health and Disability Ethics Committees, dated 9 October 2019)

Results

This study was comprised of 750 Medical students and 4722 Science students. Student distribution by secondary school quintiles (Table 1 and Figure 1) demonstrates that both Medicine and Science students from higher quintiles were over represented, as 59.6% and 36.7% (respectively) came from the highest quintile compared to 1.2% and 2.9% (respectively) of the students who came from the lowest quintile.

Figure 1. Proportion of students within programme by secondary school quintile.

Table 1. Student distribution by secondary school quintile by programme.

Secondary school quintile	Medicine		Science
	N	%	N	%
1	9	1.2%	138	2.9%
2	45	6.0%	495	10.5%
3	78	10.4%	963	20.4%
4	171	22.8%	1395	29.5%
5	447	59.6%	1731	36.7%
Total	750	100.0%	4722	100.0%

The results from the repeated measures analysis (Table 2) demonstrate that in both the Medicine and Science programmes, the interactions between year of study and secondary school quintile were statistically significant. The repeated measures were used to compare the difference in StdS across the five quantiles and across the year of study (Field 2018, 839–900). The difference between the programmes was that in the Science programme, the year of study has a significant impact on mean student StdS, whereas no such an impact was observed in Medicine. Of note, no direct impact of school quintile on StdS was found in either the Medicine or the Science programme.

Table 2. Repeated measure analysis: Impact of school quintile and year of study on academic achievement.

	Medicine					Science
Effect	Num DF	Den DF	F-value	Pr > F	Sig.	Num DF	Den DF	F-value	Pr > F	Sig.
School quintile	4	748	2.05	0.0851	n.s.	4	4713	1.35	0.2477	n.s.
Year of study	4	748	0.65	0.6247	n.s.	2	4713	3.83	0.0217	<.05
Quintile*Year	16	748	2.04	0.0094	< .05	8	4713	2.15	0.0285	< .05

A closer look at the changes in StdS over the years of study by secondary school quintiles reveals that in Medicine the lowest quintile had lower StdS (P > .05 n.s.) than other quintiles in their first year in the programme (Y2); and then the caught up to the rest of the programme (Figure 1). In the Science programme, however, the lowest quintile started with the lowest StdS in the first year of study but continued to improve throughout the years (Figure 1). Note that the difference in the StdS in Science between the first and the third year was statistically significant (P < .05).

Of interest are additional differences between the Medicine and the Science programmes. In Science, with the exception of the lowest quintile, all other students, on average performed similarly over time (Figure 1). On the other hand, in Medicine the third quintile performed better than all other quintiles in the last two years, with an average StdS which was statistically significant over the mean (Y5: 522, 95%CI 501–545; Y6 528 95%CI 506–550; Figures 2 and 3).

Figure 2. Academic achievement by school quintile by year of study – Medicine.

Figure 3. Academic achievement by school quintile by year of study – Science.

In summary, in both programmes the students coming from lowest secondary school quintiles performed less well in their first year in the programme but then improved throughout their years of study. The main difference between the programmes, however, was that in Medicine the year of study did not have significant impact on a student’s StdS, whereas in science the lowest quintile exceeded performance of the other quintiles by year 3.

Discussion

The main findings of this study demonstrate that both Medicine and Science programmes mitigate some of the academic disadvantage of students from low SES schools (lowest quintile). Nonetheless, each programme impacts the students in a different way. Overall school quintile did not have any significant impact on student StdS (Table 2) but the interaction between year of study and school quintile did have a significant impact. Science students who came from the lowest quintile had lower StdS in their first year and then their StdS improved throughout the programme while outperforming all other students in their third year. This finding is important as it indicates that school SES quintile is likely to be associated with knowledge and skills in Science but once students are in the science programme, that disadvantage disappears. A plausible explanation to this finding is that low SES schools offer fewer high level of science-related subjects (Turner et al. 2010), which indicates that the educational disadvantage they experience is more related to educational resources available in school rather than at their homes. Ending up with the highest StdS indicates that the students from lowest SES quintile have the same potential to achieve as other students and once the educational resources are made available to them by the universities they flourish.

A different pattern was observed among the Medicine students. Although the lowest quintile started in their first year in the Medicine programme slightly behind the others, the differences in StdS among the students coming from different quintiles across years of study were not significant. The difference between the Science and Medicine programmes might be due to the selection process. Selection to Science is made by student National Certificate of Educational Achievement (NCEA) results only, whereas for Medicine, students first need to be successful in securing a place in Health Sciences (based on their NCEA results). Then, after a year of study in Health Sciences they are selected to the Medicine programme by their first year university GPA, an aptitude test (Andrich et al. 2017), and in the case of one university, a selection interview. This year-long selection process is likely to have a similar moderating impact on the low school quintile students as the first year in Science had on their low quintile students.

Implications for policy and practice

Although students coming from lower SES school quintiles demonstrate different patterns of attainment over their study across the two programmes, they share one important trend: once enrolled in the programme they perform well. This finding is critically important and suggests that prior educational disadvantage can be mitigated in higher education, at least in the Science and the Medicine programmes. The implications of this are far-reaching. There is a need to increase the number of students coming from low SES schools in these highly competitive programmes, since they are likely to be as successful as their counterparts from higher SES schools (Tumen, Shulruf, and Hattie 2008). The findings in this study demonstrate that only 1.2% and 2.9% of the students in Medicine and Science (respectively) came from the lowest SES quintile. It is inconceivable to assume that so few students in low SES schools are talented enough to study Medicine and Science, which raises the question why that happens. Plausible explanations are that students in low SES schools are not sufficiently motivated or encouraged to apply to highly competitive academic programmes (Vernon et al. 2018); or that the educational environment in low SES schools is not supportive enough to enable and encourage their students to study Medicine and Science (and perhaps similar programmes) at the university (Van den Broeck et al. 2023; Vandelannote and Demanet 2021b; 2021a). It is also possible that the selection process places more weight than is desirable on the curricular knowledge obtained in secondary school compared to a student’s intellectual skills (Turner et al. 2010). Such a selection process may augment the negative impact low SES schools may have on talented students. Although further research into these effects is desirable, the findings of this study suggest that selection policies to highly competitive programmes, particularly Medicine and Science, need to be reviewed in order to allow more talented students from disadvantaged secondary school to enrol and succeed in these programmes.

This study has some strengths and limitations. Its strength is that it uses national data (New Zealand) which is rarely used for such studies. Using National data minimises the risk of obtaining results from a single institute which may not be sufficiently representative, limiting the generalisability of the outcomes. On the other hand, the integrated data, held by Statistics New Zealand, does not include tertiary (university) grades but rather pass/fail mark per course. This led us to generate the ‘Standardised Score’ (StdS) to substitute course marks. The limitation of the StdS is that it does not provide detailed information about the level of performance within a course, yet its advantage is that it considered the likelihood for passing a course, i.e. a measure of course difficulty. This advantage is important since it mitigates the impact of ‘easy’ or ‘difficult’ course on the analysis and the outcomes.

It is also noted that the low participation rate of students from low SES may impact the statistical significance. However, this is a national study of four consecutive cohorts of students, thus despite the limited statistical power available for the low SES students, we are confident that the results appropriately represent the relevant NZ student population, since it is a sample of the entire relevant population and there is no reason to suspect that a different set of four national cohorts would yield different results.

An additional limitation is that the available SES data did not include all personal student characteristics deemed to be relevant to the study objectives, which focused on a school’s rather than a student’s SES. Future studies should look into student personal characteristics and their influence on the academic achievement.

In conclusion, the results of this study emphasise the importance of encouraging students from under-represented groups to study in highly competitive academic programmes such as Science, Medicine and possibly others. Enhancing equity should not be limited to open the university gates to under-represented groups in general, but rather it must consider all possible ways to enable students from socio-economically disadvantage backgrounds to enrol in competitive programmes. Once they are already enrolled, their performance is on par with their more affluent counterparts even if it may take a year or two to get there.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by a grant received from UCAT ANZ Consortium.

Notes on contributors

Boaz Shulruf

Boaz Shulruf is Professor in Medical Education. His main research interest is in the area of psycho-educational assessment in higher education, particularly within the context of Medical and Health Sciences Education.

Sarah Tumen-Randal

Sarah Randal is Programme Director-Evidence and Evaluation at Universities New Zealand (New Zealand Vice Chancellor’s Committee) and manages an Analytics and Insights team that performs sector-level analysis on linked (person-level) administrative data to identify patterns, better predict and understand the pathways and outcomes of university students and graduates.

Phillippa Poole

Phillippa Poole is medical graduate of the University of Auckland and a specialist general physician. Professor Phillippa Poole is currently Head of the School of Medicine at her alma mater. She was head of the medical programme for nearly 10 years.

John Randal

John Randal is a Professor in the School of Economics and Finance. He is partially seconded into a role as Director of the Teaching Intensive Academic Career Pathway, and also serves as the Associate Dean (Students) in the Wellington School of Business and Government.

Daniel Wrench

Daniel Wrench was employed by Universities New Zealand as data scientist. Currently, alongside part-time work at Harmonic Analytics (consultancy firm), he is working towards a PhD at Victoria University of Wellington, researching how to solve the problem of missing data in analyses of turbulence in the solar wind.

Tim Wilkinson

Tim Wilkinson is a consultant physician in geriatric medicine with the Canterbury District Health Board. He is also Deputy Editor of Medical Education, Section Editor of BMC Medical Education, and a member of the editorial board for Focus on Health Professional Education and Australasian Journal on Ageing. Tim is keen to promote independence in old age as well as independence in his students.