Young People’s Experiences With an Empowerment-Based Behavior Change Intervention to Prevent Sexual Violence in Nairobi Informal Settlements: A Qualitative Study
Bibliographic record
Abstract
PURPOSE: Young people in sub-Saharan Africa face one of the world's highest burdens of sexual violence. Previous impact evaluations indicated that a 6-week empowerment-based behavioral intervention in Nairobi informal (slum) settlements can reduce sexual assault. This qualitative study investigated girls' and boys' experiences of the intervention to identify potential mechanisms of change. METHODS: We conducted a qualitative study in Nairobi slums with students (aged 15-21 years) who had participated in 2 parallel school-based curriculums called IMPower (girls) and Your Moment of Truth (boys) at least 1 year ago. Data were collected via 10 focus group discussions (5 for boys, 5 for girls) with 6-11 participants in each and 21 individual in-depth interviews (11 boys, 10 girls) that explored participants' experiences of the intervention and their suggestions for improvement. Findings were analyzed using thematic network analysis guided by empowerment theory. RESULTS: Girls described how the intervention enabled them to recognize and resist sexual assault via verbal and physical strategies for self-protection, negotiate sexual consent, and exercise agency. Boys described increased ability to avoid risky behaviors and "bad" peer groups and to understand and respect consent. Girls also described how the intervention strengthened their self-confidence, and boys said that it boosted positive life values and gender-equal attitudes. Skilled facilitators and interactive and relevant content were highlighted as key to intervention success. Areas of improvement included expanding the curriculum to contain more content on sexual and reproductive health and rights and involving out-of-school youth, parents, teachers, and communities. CONCLUSION: Findings indicate that a relatively short, behavioral school-based intervention can empower both girls and boys to prevent various forms of sexual violence in a low-income setting where it is endemic. Incorporating multilevel support structures, such as involving communities and families, could further enhance young people's long-term safety, health, and well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".