Equipping community health workers in Rwanda to deliver a gender transformative parenting program to prevent violence against women and children at scale
Bibliographic record
Abstract
Introduction In Rwanda, the Bandebereho program has demonstrated long-term reductions in intimate partner violence (IPV) and violence against children. Since 2019, the program has partnered with government to train community health workers (CHWs) to deliver at scale. Evidence on how to equip CHWs to deliver Bandebereho, or similar programs, with quality and fidelity is needed to support scaling. This study sought to assess the impact of training on CHWs and their capacity to deliver Bandebereho during scale up. Methods A pre/post, follow-up study was conducted with 573 CHWs in Burera district. Data were collected at three time points over 20 months using self-administered questionnaires (pre/post) and a follow-up phone survey. Questionnaires gathered data on CHW attitudes about gender roles and violence, self-reported skills, knowledge and confidence to implement Bandebereho, and training impacts on partner relations and community work. Informed consent was obtained from all study participants. Results The pre-survey was completed by 562 CHWs and 564 CHWs completed the post-survey after six to nine months. The phone survey was administered to 506 CHWs at follow-up (at 17–19 months). Analysis of changes between pre- and post-surveys found CHWs had more equitable gender attitudes after the training. Linear regression analysis found that CHWs with some secondary education (coefficient: −2.15, p < 0.01) and more than three years' experience (coefficient: −2.27, p < 0.001) were less likely to hold inequitable attitudes. At post-survey, CHWs reported a high level of preparedness to implement, regardless of gender. A majority reported improved partner relations, including greater partner support for their community work. At follow-up, a majority of CHWs reported a high degree of comfort and confidence implementing Bandebereho, and benefits to their work and personal relationships. Conclusions The findings highlight the importance of investing in high-quality facilitator training, which allows sufficient time for facilitators' own transformation, to maintain quality and fidelity at scale. The findings underscore the importance of a slow and steady approach, with sufficient time to adapt, test, and refine IPV programs for scale, which can also support a progressive handover to government. The findings may support program originators who seek to scale proven IPV prevention programs with government in other settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".