Objective 1

To meet Objective 1, I would need to investigate and answer my first research question: What was the difference between the graduation rate of men and women medical students compared to their entry rates into SA medical practice between 1996 and 2005?

3.3.1 Secondary data collection

In this part of the research project, I will be using secondary data, which involves the collation and exploration of existing datasets, as well as an extensive review of data gathered in previous studies on the subject.

Stated differently, this part of the research concentrated on the review of data, including:

• Data on the numbers and distribution of medical practitioners in South Africa from 2002 to 2006, and in 200849, as supplied by the Health Professions Council of South Africa (HPCSA)

• Data on medical school enrolments and graduations in 199650, and from 1999 to 2006, as supplied by the Department of Education’s51 Higher Education Management Information System (HEMIS)

• Data relating to the numbers and distribution of healthcare practitioners from the Health Systems Trust (HST) 2000 – 2008 and Labour Force Survey

49

I have repeatedly attempted to obtain gender disaggregated professional data from the HPCSA, but have not received this data.

50

I had access to gender and race disaggregated medical student data from HEMIS for the period 1999 to 2005 (the period of investigation for this study). However, I wanted to extend this analysis in a few tables to report on a trend over 10 years, and thus only requested data for 1996. The years in between would not have made a difference, as the formula used in the relevant tables only took into account the start (1996) and end (2005) years.

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This department has subsequently (since 2008/9) been divided into two: the Department of Higher Education and Training (DHET), and the Department of Basic Education (DBE).

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• Data on medical student enrolment and graduation in 1996 and from 1999 to 2005, as well as medical student cohort data from 1996 to 2005, will be used, as obtained from the Institutional Planning Department for the UCT case study.

I will briefly discuss how the UCT case study will be used in an effort to meet Objective 1, and will later elaborate on how it will be used in an effort to meet Objective 2.

UCT was chosen as a suitable case for further investigation into gendered trends observed in national medical school and profession data for two reasons. Firstly, as explained in the introduction, this case was part of the HSRC’s study on the medical profession in SA, and I already had access to a large amount of data from this university that had not been explored to assess the reasons behind the gendered trends observed. Secondly, the cohort data of the larger study was not disaggregated by sex, and my study offered a vehicle by which to specifically explore the reasons behind the possible gendered trends. Again, my analytical position was from an intersectional perspective, and thus my interest was not only to establish the factors impacting on women doctors, but also to highlight the intersection between race and gender. UCT’s medical school was a perfect case to explore race and gender transformation in medical education, as it is “a historically white English-speaking university that has undergone considerable change in the composition of its student body and curriculum in recent years” (Breier & Wildschut, 2006: 7).

Thus, in an effort to meet Objective 1 I would use UCT enrolment and graduation data, as well as cohort data. This assisted in establishing the sex trends in attrition at UCT’s medical school, thus making possible an in-depth exploration of specific outcomes for medical students within a specific context.

3.3.2 Secondary data analysis

The quantitative data analysis involved the secondary analysis of existing data sets (HEMIS, HPCSA, HST, LFS and UCT cohort data), referred to previously.

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This part of the study thus involved an analysis of national enrolment and graduation trends (DoE HEMIS database) and their comparison with cohort student data from the UCT medical school. It will also involve a comparison with data from the Health Professions Council of South Africa (HPCSA) to establish the rate of attrition between graduation and registration as a general practitioner. The former will be undertaken in order to identify and describe the national trends in attrition for males and females, while the latter will focus on identifying the sex trends in attrition evident specifically at UCT medical school. Professional data disaggregated by race and sex were also requested from the HPCSA to allow an investigation of how these variables might influence attrition.

Furthermore, the UCT case study will highlight whether the broader national trends are also evident and experienced in the same way. This most closely approximates Denzin and Lincoln’s (2003: 137) description of an instrumental case study where “a particular case is examined mainly to provide insight into an issue”. Thus, the case plays a supporting role in the sense that it is supposed to give us insight into a phenomenon. As Mason (2002: 175) points out, qualitative research is particularly good at supporting arguments that focus on “how social phenomena and processes operate or are constituted”. This will also give us a sense of the extent to which this case study might be illustrative of the national population, although, because of the small sample size, it cannot be considered appropriate for empirical generalisation purposes.

However, there is an opportunity for theoretical generalisation based on two claims. Firstly, it would be possible to generalise because I have no reason to assume that my sample, and thus my analysis, are atypical (Mason, 2002). Secondly, UCT was selected specifically because it had the highest proportion of women medical students at any medical school in South Africa, and thus would present the most appropriate case to investigate issues associated with the feminisation of medical education in SA. In this way it constitutes a pivotal case (Mason, 2002), which is important not only for exploring, but offering an explanation of, issues (such as attrition and discrimination) underlying the feminisation of the medical profession in SA. The only way that I would be able to make stronger claims to theoretical generalisability would be if I could

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establish that the sample of respondents in this study is representative of the wider population to which they belong. However, as established before, and based on the poor quality of racial and sexual disaggregated professional data, I cannot establish representivity accurately.