“Seeing Is Believing”: Identifying the Sexual and Reproductive Health Priorities of Adolescent Girls and Young Women in Freedom Park, South Africa Through an Adapted Body Mapping Approach
Bibliographic record
Abstract
Objectives: This study uses a decolonized research approach to identify the sexual and reproductive health (SRH) priorities of adolescent women living in Freedom Park, Cape Town, South Africa. The history of colonialism and apartheid has a significant ongoing impact on the SRH of women in the community. The objectives of the research were for adolescent women to create a shared definition of SRH and identify SRH needs and priorities. Methods: A qualitative, participatory action design guided by decolonized methodologies was employed. Community members co-developed a modified body mapping exercise, ensuring cultural appropriateness and participant privacy. This participatory tool was used to explore SRH issues, leveraging its ability to foster dialogue and self-expression in a safe and collaborative environment. Seven workshops were conducted, engaging 54 adolescent girls and young women (AGYW) aged 16-25. Participant body maps and narratives were analyzed with the community through thematic coding and visual interpretation. Results: Participants defined SRH, and illustrated SRH body parts, outcomes, and priorities on their body maps. Five themes were identified when discussing priority SRH issues: reproductive health and sexual wellness, abuse and violence, mental health, support and knowledge, and social pressures. Participants identified the two SRH issues they most wanted to address in their community as gender-based violence (GBV) and adolescent pregnancy. The body mapping methodology fostered open discussion and provided insight into personal lived experiences. Conclusions: This study highlights socio-economic factors, cultural context, and historical influences as intersecting root causes of SRH outcomes in Freedom Park. The participatory body mapping approach empowered AGYW to express their SRH needs and identify community-driven priorities. Findings underscore the importance of contextualized, culturally sensitive research methods in addressing complex health challenges. Future interventions should address GBV and adolescent pregnancy through community-led strategies to foster sustainable change.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".