Access to health care in South Africa--the influence of race and class.
Bibliographic record
Abstract
OBJECTIVES: The first democratic government elected in South Africa in 1994 inherited huge inequities in health status and health provision across all sections of the population. This study set out to assess the impact of the new government's commitment to address these inequities and implement policies to improve population health in general and address inequalities in health care in particular. DESIGN: A 1998 household survey assessed many aspects of health delivery, including their own perceived and actual access to health care among different segments of South African society. RESULTS: Race was the main predictor of perceived changes in access to health care, with black, coloured-and Indian respondents significantly more likely to feel that access had improved since 1994, compared with white respondents. Socio-economic status (SES) was the main predictor of actual access to health care, with low and middle SES classes significantly less likely to access care when ill. CONCLUSIONS: One-third of respondents perceived health care access to have improved between 1994 and 1998, and this response was partially determined along racial lines. About one-quarter reported an inability to access health care when they required it, and this response was partially determined along socio-economic lines. This set of contrasting responses suggests that at a political level perceptions are largely influenced by race, but at the operational level actual access is influenced by SES.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".