How culture shapes the sexual and reproductive health practices among adolescent girls in Eastern Equatoria, South Sudan
Bibliographic record
Abstract
Background: From 2011 to 2014, a programme aimed to improve sexual and reproductive health practices among adolescent girls was implemented by the non-governmental organization BRAC in partnership with the Government of South Sudan and with funding from the World Bank. The distinctive culture and norms in South Sudan offered unique challenges to the implementation of such a programme, which this study sought to explore. Objectives: This study investigated the sexual and reproductive health (SRH) behaviour of women and men in Eastern Equatoria State to identify the social norms and beliefs that affect communities’ perception and behaviour regarding SRH issues. Methods: Data were gathered from seven key informant interviews and nine focus groups of adult women and men and adolescent girls and boys in Torit and Magwi Counties in Eastern Equatoria. Results: The study found a strong cultural preference for girls to demonstrate their fertility by beginning to have children at an early age (13–16 years) and to have many children (8–12). It also found that education on HIV/AIDS had been effective. Conclusions: To be effective in South Sudan, adolescent sexual and reproductive health programmes must take the current social norms and practices into account and learn from the successes of HIV/AIDS education programmes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".