The influence of women’s groups mobilisation on health emergency response: Evidence from the Ebola outbreak in Liberia
Bibliographic record
Abstract
BACKGROUND: The 2014-2016 Ebola outbreak in West Africa showed that multiple response strategies are necessary to contain disease outbreaks in resource-constrained settings. A critical component of these response strategies was the involvement of community members and women's groups in leading them. While women's groups actively participated in Ebola containment strategies in various communities, there is a dearth of research on their role or how their presence in communities affected the deployment of Ebola response strategies. In contributing to bridging this knowledge gap, we ask: How did the presence of women's groups influence perceptions of Ebola response strategies in Ebola-affected communities in Liberia?. METHODS: We fitted multivariate multinomial logistic regression models to cross-sectional data (n = 1,340) collected in five counties in Liberia. We built a composite model from community response strategies that participants reported witnessing. These responses were then categorized into no response, single-response, and multiple-response strategies. Response strategies included Ebola education and sensitisation campaigns, community surveillance, lobbying for personal protective equipment (PPEs), and other measures (e.g., prayer). Single responses pertain to participants choosing only one response strategy, while multiple responses indicate the selection of two or more response strategies. RESULTS: Overall, we found that knowledge about the presence of women's groups was associated with an increased likelihood of participants reporting having witnessed a community response during the Ebola outbreak. Specifically, participants who reported having women's groups in their communities had 89% and 98% higher odds of reporting a single Ebola response (RRR = 1.89, p ≤ 0.001) and multiple responses (RRR = 1.98, p ≤ 0.001), respectively. We also found some demographic, socioeconomic, and place-based variables to be associated with Ebola response strategies. CONCLUSION: We provide relevant policy recommendations necessary to center women's groups and other community organisations in Liberia's strategic health plan toward a pandemic-ready future. We believe that strengthening local, national, and international collaborations is critical to achieving this future and can help the country reach its SDG 3.3 goal of ending infectious diseases by 2030.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".