DO OLDER ADULTS WITH HIV HAVE A SOCIAL NETWORK DEFICIT? EVIDENCE FROM AGINCOURT, SOUTH AFRICA
Bibliographic record
Abstract
Abstract HIV/AIDS has had a substantial social and economic impact on Sub-Saharan Africa, and research is only beginning to examine the prevalence and consequences of HIV infection among older adults in this region. Though informal social networks provide crucial resources for older people managing health problems, little is known about how the form and function of such networks differs by HIV status. Drawing from theories of health stigma and network mobilization, we use egocentric network data from HAALSI, the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (N=5,059). HAALSI is a community-based study centered in Agincourt, South Africa, and focuses on adults ≥40 years of age. Approximately 12% of this sample is HIV positive. Results of multivariable logistic and Poisson regression reveal three main findings. First, relative to those without HIV, infected older adults have larger personal networks—including more kin and more non-kin network members. Second, HIV status has no discernible impact on whether people receive regular emotional support from those in their networks. Third, older adults who have disclosed their HIV status have a relatively high proportion of kin members in the close networks relative to those not infected with HIV and to those with HIV who have not disclosed their disease. Our findings point to the need for further research on the implications of social networks for outcomes such as well-being and health care delivery among older AIDS patients in the Global South.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".