Ebola and community health worker services in Kenema District, Sierra Leone: please mind the gap!
Bibliographic record
Abstract
Setting: All community health workers (CHWs) in rural Kenema District, Sierra Leone. Objective: CHW programmes provide basic health services to fill gaps in human health resources. We compared trends in the reporting and management of childhood malaria, diarrhoea and pneumonia by CHWs before, during and after the Ebola outbreak (2014–2016). Design: Retrospective cross-sectional study using programme data. Results: CHW reporting increased from 59% pre-outbreak to 95% during the outbreak ( P < 0.001), and was sustained at 98% post-outbreak. CHWs stopped using rapid diagnostic tests for malaria mid-outbreak, and their use had not resumed post-outbreak. The average monthly number of presumptive treatments for malaria increased from 2931 pre-outbreak to 5013 during and 5331 post-outbreak ( P < 0.001). The average number of monthly treatments for diarrhoea and pneumonia decreased from respectively 1063 and 511 pre-outbreak to 547 and 352 during the outbreak ( P = 0.01 and P = 0.04). Post-outbreak pneumonia treatments increased (mean 1126 compared to pre-outbreak, P = 0.003), and treatments for diarrhoea returned to pre-outbreak levels ( P = 0.2). Conclusion: The CHW programme demonstrated vulnerability, but also resilience, during and in the early period after the Ebola outbreak. Investment in CHWs is required to strengthen the health care system, as they can cover pre-existing gaps in facility-based health care and those created by outbreaks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".