Disparities in quality of life by race, gender, and sexual orientation: An intersectional analysis of population-representative data in Gauteng, South Africa
Bibliographic record
Abstract
South Africa's history of apartheid has led to persistent inequalities. While progress has been made since 1994, disparities in quality of life (QoL) remain, particularly along racial lines. This study examines how race, gender, and sexual orientation intersect to influence QoL in Gauteng - South Africa's most populous and economically vibrant province. Using data from the Gauteng City-Region Observatory's QoL 6 (2020/2021) Survey, we analyzed a sample of 10,760 respondents. We employed inverse probability weighting with regression adjustment (IPWRA) to estimate the Average Treatment Effect (ATE) of race, gender, and sexual minority status on QoL, while controlling for socioeconomic factors. Significant QoL disparities were observed across intersecting identities. White heterosexual men had the highest QoL scores, while Black sexual minority women had the lowest. After adjusting for covariates, all Black groups exhibited significantly lower QoL scores compared to their White counterparts. The largest gap was between White sexual minority women and Black sexual minority men (ATE: -14.47; 95%CI: -17.18,-11.76). Within the Black population, heterosexual men had significantly higher QoL than heterosexual women (ATE: -0.98; 95%CI: -1.54, −0.42). Despite progress since apartheid, substantial QoL disparities persist in Gauteng, primarily along racial lines, particularly in access to services and socio-economic opportunities. The intersectionality of race, gender, and sexual orientation creates distinct vulnerabilities, particularly for Black sexual minority women. These findings suggest that current policies aimed at improving equity may be insufficient. Addressing these disparities requires a multifaceted approach that considers the complex interplay of race, gender, and sexual orientation in shaping QoL. • Examined Quality of Life (QoL) disparities across intersecting identities in post-apartheid South Africa. • Used doubly-robust method to balance socioeconomic factors across identity groups. • Finds persistent QoL gaps between White and Black South Africans across orientations. • Black sexual minority women face the largest QoL disparities in Gauteng province. • Results highlight need for intersectional approach in South African equity policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".