A survey of trainee specialists experiences at the University of Cape Town (UCT): Impacts of race and gender
Bibliographic record
Abstract
BACKGROUND: Efforts to redress racial and gender inequalities in the training of medical specialists has been a central part of a dedicated programme in the Faculty of Health Sciences at the University of Cape Town (UCT). This study aimed to describe trends in race and gender profiles of postgraduate students in medical specialties (registrars) from 1999 to 2006 and to identify factors affecting recruitment and retention of black and female trainees. METHOD: Review of faculty databases for race and gender data from 1999 to 2006. Distribution of an anonymous self-administered questionnaire to all registrars in 2005/2006. RESULTS: The percentage of African registrars doubled from 10% to 19% from 1999 to beyond 2002. The percentages of Africans, Coloureds and Indians rose steadily from 26% to 46% from 1999 to 2005, as did that of women from 27% to 44%. The institution's perceived good reputation, being an alumnus and originating from Cape Town were common reasons for choosing UCT for training. A quarter of respondents reported knowledge of a friend who decided against studying at UCT for reasons which included anticipated racial discrimination. Black respondents (23%), particularly African (50%), were more likely to describe registrarship at UCT as unwelcoming than white respondents (12%). Specific instances of personal experience of discrimination were uncommon and not associated with respondents' race or gender. Registrars who had had a child during registrarship and those reporting discrimination were more likely to rate the learning and research environment as poor (Odds Ratio, 4.01; 95% CI 0.98 - 16.47 and 1.99 95% CI 0.57 - 6.97, respectively). CONCLUSION: The proportion of black and female registrars at the University of Cape Town has increased steadily from 1999 to 2006, most likely a result of systematic equity policies and procedures adopted in the faculty during this period. The data point to a need for policies to make the institution more welcoming to diversity and for strategies to address institutional culture and mentorship, with an aim to develop examples of best practices to share within and between institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".