The financial impact of HIV/AIDS on poor households in South Africa
Bibliographic record
Abstract
BACKGROUND: Rising mortality rates caused by HIV/AIDS in South Africa have substantial and lingering impacts on poor households. METHODS: This is a descriptive paper using a new dataset of daily income, expenditure and financial transactions collected over a year from a total of 181 poor households in South African rural and urban areas. One of the key pathways through which HIV/AIDS impacts on household wellbeing is through the socioeconomic impacts of death, which this dataset is especially useful in quantifying. RESULTS: The key impacts of death on households are funerals and the loss of income. Funerals often cost up to 7 months of income. Nearly all households in the sample attempt to cover such costs by holding a portfolio of funeral insurance. Despite these efforts to insure against funeral costs, 61% of households are underinsured against the cost of a funeral. Nearly half the sample households are dependent on a regular wage earner, and another quarter are dependent on a grant recipient. Eighty per cent of these households would lose over half of their monthly income should the highest income recipient in the household die. Even by selling liquid assets, only one third of the sample households would be able to maintain their pre-death living standards for a year or more. CONCLUSION: Death poses substantial and lingering burdens from the funerals that surviving household members need to finance and the ongoing loss of income once brought into the household by the deceased. These costs pose so great a threat to households that they dominate household saving and insurance behavior.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".