Community-based health care is an essential component of a resilient health system: evidence from Ebola outbreak in Liberia
Bibliographic record
Abstract
BACKGROUND: Trained community health workers (CHW) enhance access to essential primary health care services in contexts where the health system lacks capacity to adequately deliver them. In Liberia, the Ebola outbreak further disrupted health system function. The objective of this study is to examine the value of a community-based health system in ensuring continued treatment of child illnesses during the outbreak and the role that CHWs had in Ebola prevention activities. METHODS: A descriptive observational study design used mixed methods to collect data from CHWs (structured survey, n = 60; focus group discussions, n = 16), government health facility workers and project staff. Monthly data on child diarrhea and pneumonia treatment were gathered from CHW case registers and local health facility records. RESULTS: Coverage for community-based treatment of child diarrhea and pneumonia continued throughout the outbreak in project areas. A slight decrease in cases treated during the height of the outbreak, from 50 to 28% of registers with at least one treatment per month, was attributed to directives not to touch others, lack of essential medicines and fear of contracting Ebola. In a climate of distrust, where health workers were reluctant to treat patients, sick people were afraid to self-identify and caregivers were afraid to take children to the clinic, CHWs were a trusted source of advice and Ebola prevention education. These findings reaffirm the value of recruiting and training local workers who are trusted by the community and understand the social and cultural complexities of this relationship. "No touch" integrated community case management (iCCM) guidelines distributed at the height of the outbreak gave CHWs renewed confidence in assessing and treating sick children. CONCLUSIONS: Investments in community-based health service delivery contributed to continued access to lifesaving treatment for child pneumonia and diarrhea during the Ebola outbreak, making communities more resilient when facility-based health services were impacted by the crisis. To maximize the effectiveness of these interventions during a crisis, proactive training of CHWs in infection prevention and "no touch" iCCM guidelines, strengthening drug supply chain management and finding alternative ways to provide supportive supervision when movements are restricted are recommended.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".