Similar health emergencies, different commitments: Comparative strategies to end Ebola and COVID-19 in “post-conflict” Liberia
Bibliographic record
Abstract
Liberia, in the face of two consecutive health emergencies - the Ebola epidemic in 2014 and COVID in 2019 - offers a unique, comparative perspective on health crisis management within a fractured healthcare system. In dialogue with a feminist-informed political economy of health in the African context, this paper has two central objectives. First, it examines the strategies employed by community-based women's organisations - many of whom remain invested in peacebuilding after a 14-year civil war (1989-2003)) - to contain the Ebola and COVID-19 disease outbreaks. Second, it explores the implementation strategies under two political administrations, Sirleaf (Ebola) and Weah (COVID-19), at two distinct political moments. Results from five focus group discussions (n = 27) and seven in-depth interviews (n = 7) suggest that, while there was a relative collective effort from the Liberian government, grassroots women's organisations and community members to contain the Ebola epidemic response, the COVID-19 response witnessed an individualistic approach. Overall, participants suggested that lessons learned from the Ebola epidemic did not seem to be transferred to managing the COVID-19 pandemic in Liberia. The study suggests that while local-government-international partnerships are instrumental in ending health emergencies, grassroots community organisations require economic and social resources and sustained political will to effectively build and maintain various health infrastructures in post-conflict countries. This is relevant not just for managing disease outbreaks and health emergencies but also for entrenching public health services to support population health. Here, lessons from Ebola and COVID-19 rooted in everyday experiences of women's reproductive labour can provide an educational foundation for responding to future disease outbreaks in Liberia and other post-conflict contexts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".