Effects of a gender-responsive maternal, newborn and child health program on health and economic outcomes during COVID-19 in Kenya: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic worsened health and economic disparities for women in resource-limited settings. Chamas for Change (Chamas, Swahili for 'groups with a purpose') is a gender-responsive maternal, newborn and child health program that combines health education with social support and microfinance activities to address systematic disparities in maternal and infant health outcomes. This study evaluated the program's effectiveness in mitigating pandemic-related health and economic inequities in Trans-Nzoia County, Kenya, a region with significant pre-existing vulnerabilities. METHODS: We conducted a mixed-methods study using an explanatory sequential design from March to December 2023. We collected quantitative data from 609 women in 3 cohorts: continuous Chamas participants (n = 128), discontinued (drop-out) participants (n = 240), and women without Chamas exposure (n = 241). We measured maternal health indicators and the Poverty Probability Index (PPI) score as primary outcomes. Quantitative analysis included linear mixed-effects models (unadjusted and adjusted). Qualitative data from focus group discussions and key informant interviews (n = 57) were analyzed using the Gender and COVID-19 Matrix. RESULTS: Continuous Chamas participants achieved significantly higher rates of postpartum visits (OR = 19.54; 95% CI:3.76-101.57) and exclusive breastfeeding (OR = 8.04; 95% CI:1.52-42.43), demonstrating reduced disparities in essential maternal health services. They showed lower health insurance uptake (OR = 0.43; 95% CI:0.22-0.83) and minimal improvements in PPI scores. Qualitative findings revealed that while the pandemic disrupted health services, Chamas membership provided continuity of care through adapted CHW services. However, pandemic-related restrictions limited the program's economic benefits, potentially due to the program's shifted focus toward health service delivery during the crisis, intensifying existing economic inequities. CONCLUSION: The Chamas program effectively sustained maternal and child health practices during the COVID-19 pandemic through adapted CHW support but showed limited ability to protect members from economic hardship. This demonstrates both the resilience and limitations of community-based interventions during widespread crises. Our results highlight the need for robust governmental support and social protection measures to address underlying economic vulnerabilities for women. Future pandemic preparedness should integrate CHWs into formal health systems and focus on strengthening linkages with formal financial systems while supporting CHWs' role in reducing inequities in maternal and newborn health service delivery during crises.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".