Gender-Based Violence Awareness Campaigns in Kenyan Schools: Programme Adoption and Educational Outcomes Evaluation
Bibliographic record
Abstract
Gender-based violence (GBV) is a pervasive issue in Kenyan schools, impacting both students and educators. The need for effective GBV awareness campaigns has led to initiatives aimed at enhancing knowledge and attitudes towards GBV. A mixed-methods approach was employed, including a quantitative survey among students and teachers in selected schools, alongside qualitative interviews with school administrators. Data were collected through online surveys and face-to-face interviews, ensuring comprehensive coverage of GBV knowledge levels and implementation strategies. The findings indicate that while over 80% of participants reported increased awareness about GBV after the campaign, only a quarter demonstrated positive behavioural changes indicative of understanding and support for victims. Engagement varied significantly between schools with more structured programme implementations showing higher efficacy. While initial campaigns showed promising uptake in raising GBV knowledge among students and teachers, sustained engagement and tailored implementation strategies are crucial to fostering meaningful educational outcomes. Schools should develop comprehensive multi-faceted programmes that include regular training sessions, community partnerships, and ongoing support mechanisms. Educational institutions must also integrate GBV education into existing curricula for long-term impact on student attitudes and behaviors.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".