Gender differences in experiences of ART services in South Africa: a mixed methods study
Bibliographic record
Abstract
OBJECTIVES: A mixed methods study exploring gender differences in patient profiles and experiences of ART services, along the access dimensions of availability, affordability and acceptability, in two rural and two urban areas of South Africa. METHODS: Structured exit interviews (n = 1266) combined with in-depth interviews (n = 20) of women and men enrolled in ART care. RESULTS: Men attending ART services were more likely to be employed (29%vs. 20%, P = 0.001) and were twice as likely to be married/co-habiting as women (42%vs. 22%P = 0.001). Men had known their HIV status for a shorter time (mean 32 vs. 36 months, P = 0.021) and were also less likely to disclose their status to non-family members (17%vs. 26%, P = 0.001). From both forms of data collection, a key finding was the role of female partners in providing social support and facilitating use of services by men. The converse was true for women who relied more on extended families and friends than on partners for support. Young, unmarried and unemployed men faced the greatest social isolation and difficulty. There were no major gender differences in the health system (supply side) dimensions of access. CONCLUSIONS: Gender differences in experiences of HIV services relate more to social than health system factors. However, the health system could be more responsive by designing services in ways that enable earlier and easier use by men.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".