Factors Associated with Sexual Behaviour among Women Aged 15-49 in South African Low-Income Communities
Bibliographic record
Abstract
Introduction: South Africa faces disproportionately high rates of sexually transmitted diseases (STDs), especially in low-income communities. Understanding how demographic, socioeconomic, and knowledge-based factors influence sexual behaviour is critical for targeted public health interventions. Therefore, this study examined the relationships between socioeconomic status (SES), education, ethnicity, marital status, STD awareness, and sexual behaviour among women aged 15 – 49 in low-income South African communities. Methods and Materials: We conducted a cross-sectional study utilizing data from the South Africa Demographic and Health Survey 2016 (SADHS 2016). Key variables included sexual behaviour (safe versus risky), SES, education, place of residence, ethnicity, marital status, and awareness of STDs. Bivariate and multivariable analyses were used to assess associations between sexual behaviour and the aforementioned variables. Results: Among 8,513 respondents, 22.3% (95% CI: 21.1–23.5) engaged in risky sexual behaviour. Higher SES was associated with increased odds of risky behaviour, as were White, Coloured, and Indian/Asian ethnicities compared to Black Africans. Conversely, higher education levels, being married or cohabiting, and STD awareness (having heard of AIDS) significantly reduced risky sexual behaviour. Urban residence had no significant effect. These findings highlight the influence of socioeconomic and educational factors on sexual health outcomes. Conclusion: Sexual behaviour may be influenced by a number of factors, and behavioural patterns vary across groups. Public health strategies and intersectional approaches to sexual health should be considered to enhance education and STD awareness to reduce risky behaviours and improve sexual health outcomes in different population groups.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".