A multilevel mixed effect analysis of neighbourhood and individual level determinants of risky sexual behaviour among young people in South Africa
Bibliographic record
Abstract
BACKGROUND: Despite national and international commitments and efforts to prevent risky sexual behaviours, a high proportion of young people in South Africa are engaged in risky sexual behaviour. However, most efforts are currently directed toward addressing individual-level factors at the expense of not addressing neighbourhood-level determinants such as social disorganisation, contributing to risky sexual behaviour among young people in South Africa. This study investigated the multilevel factors of risky sexual behaviours among young people by gender in South Africa, using the lens of socio-ecological and social disorganisation frameworks. METHODS: Data from a nationally representative sample of 1268 males and 2621 females aged 15-24 years, giving a total of, 3889 never-married youths, were drawn from the 2016 South Africa Demographic and Health Survey. Analysis was conducted using multilevel mixed-effect logistic regressions with random community-level effects. RESULTS: Findings show that youth who were from a heterogeneous ethnic group (AOR = 0.49, CI: 0.35-0.67), household size of 5 + members (AOR = 0.78, CI: 0.54-1.15), community education (AOR = 0.97, CI: 0.72-1.32) were associated with low engagement in multiple sexual partnerships. Youths who were employed (AOR = 0.84, CI: 0.59-1.18), and from high-level community poverty (AOR = 0.76, CI: 0.58-1.00) were also associated with reduced odds of unprotected sex. In addition, older youth aged 20-24 years (AOR = 12.6, CI: 9.93-16.00); secondary education attainment (AOR = 1.01, CI 0.58-1.77); family structure (AOR = 1.37, CI: 0.75-1.15); Gauteng province (AOR = 1.45 CI: 0.92-2.28); residential mobility (AOR = 1.25, CI: 1.02-1.53), community media exposure to contraceptives (unprotected sex) (AOR = 1.38, CI: 1.09-1.76) were more likely to engage in risky sexual behaviour. CONCLUSION: The study revealed that neighbourhood and individual-level factors were important in explaining the factors associated with risky sexual behaviour among young people in South Africa. In addition, engagement in risky sexual behaviour was high, with minimal variation among young females and males in South Africa. It specifies that the practice of risky sexual behaviour is significantly associated with multilevel factors of social disorganisation that cut across gender. These results imply that there is a need to review policies of sexual risks reduction for each gender, which might help mitigate the adverse effects of social disorganisation for women and men youths in South Africa.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".