Where there is no doctor: can volunteer community health workers in rural Uganda provide integrated community case management?
Bibliographic record
Abstract
INTRODUCTION: Integrated community case management (iCCM) involves assessment and treatment of common childhood illnesses by community health workers (CHWs). Evaluation of a new Ugandan iCCM program is needed. OBJECTIVES: The objectives of this study were to assess if iCCM by lay volunteer CHWs is feasible and if iCCM would increase proportions of children treated for fever, pneumonia, and diarrhoea in rural Uganda. METHODS: This pre/post study used a quasi-experimental design and non-intervention comparison community. CHWs were selected, trained, and equipped to assess and treat children under five years with signs of the three illnesses. Evaluation included CHW-patient encounter record review plus analysis of pre/post household surveys. RESULTS: 196 iCCM-trained CHWs reported 6,276 sick child assessments (45% fever, 46% pneumonia, 9% diarrhoea). 93% of cases were managed according to algorithm recommendations. Absolute proportions of children receiving treatment significantly increased post-intervention: antimalarial for fever (+24% intervention versus +4% control) and oral rehydration salts/zinc for diarrhoea (+14% intervention versus +1% control). CONCLUSION: In our limited-resource, rural Ugandan setting, iCCM involving lay CHWs was feasible and significantly increased the proportion of young children treated for malaria and diarrhoea.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".