Community case management of malaria: a pro-poor intervention in rural Kenya
Bibliographic record
Abstract
BACKGROUND: Access to prompt and effective treatment of malaria is a fundamental right of all populations at risk; many countries have not met the target of 60% of children treated with effective antimalarial drugs within 24 h of fever onset. While community case management of malaria is effective for increasing coverage, evidence is mixed on whether it improves equity. The objective of this study was to assess whether a community case management of a malaria programme delivered by community health workers (CHW) in two districts of Kenya improved access and equity. METHODS: Data on child fever treatment practices, malaria prevention and CHW visits was collected through cross-sectional household surveys in project communities before (December 2008) and after 1 year of intervention (December 2009). Indicators were analysed by household wealth rank (grouped into poorest [bottom 20%], poor [middle 60%] and least poor [top 20%]) and survey. RESULTS: Data were available from 763 households at baseline and 856 households at endline. At endline, access to prompt and effective malaria treatment was higher compared with baseline for all groups, with the highest proportions among the poorest (67.6%) and the poor (63.2%), and the lowest proportion among the least poor (43.4%). Corresponding data suggest this was linked to the household's interaction with a CHW as the source of advice/treatment for child fever. CONCLUSION: These findings provide evidence that in a resource-poor setting, CHWs can provide lifesaving interventions to the poorest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".