Disability Grant: a precarious lifeline for HIV/AIDS patients in South Africa
Bibliographic record
Abstract
BACKGROUND: In South Africa, HIV/AIDS remains a major public health problem. In a context of chronic unemployment and deepening poverty, social assistance through a Disability Grant (DG) is extended to adults with HIV/AIDS who are unable to work because of a mental or physical disability. Using a mixed methods approach, we consider 1) inequalities in access to the DG for patients on ART and 2) implications of DG access for on-going access to healthcare. METHODS: Data were collected in exit interviews with 1200 ART patients in two rural and two urban health sub-districts in four different South African provinces. Additionally, 17 and 18 in-depth interviews were completed with patients on ART treatment and ART providers, respectively, in three of the four sites included in the quantitative phase. RESULTS: Grant recipients were comparatively worse off than non-recipients in terms of employment (9.1 % vs. 29.9 %) and wealth (58.3 % in the poorest half vs. 45.8 %). After controlling for socioeconomic and demographic factors, site, treatment duration, adherence and concomitant TB treatment, the regression analyses showed that the employed were significantly less likely to receive the DG than the unemployed (p < 0.001). Also, patients who were longer on treatment and receiving concomitant treatment (i.e., ART and tuberculosis care) were more likely to receive the DG (significant at the 5 % level). The qualitative analyses indicated that the DG alleviated the burden of healthcare related costs for ART patients. Both patients and healthcare providers spoke of the complexity of the grants process and eligibility criteria as a barrier to accessing the grant. This impacted adversely on patient-provider relationships. CONCLUSIONS: These findings highlight the appropriateness of the DG for people living with HIV/AIDS. However, improved collaboration between the Departments of Social Development and Health is essential for preparing healthcare providers who are at the interface between social security and potential recipients.
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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 | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".