Preventing Sexual Violence and Strengthening Post-Victimization Support Among Adolescents and Young People in Kenya: An INSPIRE-Aligned Analysis of the 2019 Violence Against Children Survey (VACS)
Bibliographic record
Abstract
Background: Sexual violence against adolescents and young people (AYP) remains a public health concern. This study explores patterns of sexual violence and help-seeking behaviour as well as their associated risk/protective factors with guidance of a technical package (INSPIRE) designed to reduce sexual violence in low-resource settings. Methods: The 2019 Violence Against Children Survey (VACS) dataset comprises 788 males and 1344 females. After describing the prevalence and patterns of sexual violence and help-seeking behaviour (informal disclosure, knowledge of where to seek formal help, seeking formal help, and receipt of formal help) among 13- to 24-year-old AYP, logistic regression models were then fitted to predict past-year sexual violence and informal disclosure among adolescent girls and young women (AGYW). Results: More young women than young men informally disclosed sexual violence experience (46% versus 23%). Gender inequitable attitudes [AOR 3.07 (1.10–8.56); p = 0.03], experiencing emotional violence at home [AOR 2.11 (1.17–3.81); p = 0.01] and cyberbullying [AOR 5.90 (2.83–12.29); p = 0.00] were identified as risk factors for sexual violence among AGYW. Life skills training [AOR 0.22 (0.07–0.73); p = 0.01] and positive parental monitoring [AOR 0.31 (0.10–0.99); p = 0.05] were found to be protective against sexual violence among AGYW. Positive parental monitoring [AOR 3.85 (1.56–9.46); p = 0.00] was associated with an increased likelihood of informal disclosure among AGYW. Conclusions: As Kenya intensifies efforts towards sexual violence prevention, this study underscores the need to develop and strengthen policies and programs on life skills training, cultural norms, and positive parenting, as well as improve awareness and access to post-violence response and support services.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".