Uptake of community health care provision by community health entrepreneurs for febrile illness and diarrhoea: a cross-sectional survey in rural communities in Bunyangabu district, Uganda
Bibliographic record
Abstract
OBJECTIVE: To assess the uptake of services provided by community health workers who were trained as community health entrepreneurs (CHEs) for febrile illness and diarrhoea. DESIGN: A cross-sectional survey among households combined with mapping of all providers of basic medicine and primary health services in the study area. PARTICIPANTS: 1265 randomly selected households in 15 rural villages with active CHEs. SETTING: Bunyangabu district, Uganda. OUTCOME MEASURES: We describe the occurrence and care sought for fever and diarrhoea in the last 3 months by age group in the households. Care provider options included: CHE, health centre or clinic (public or private), pharmacy, drug shop and other. Geographic Information Ssystem (GIS)-based geographical measures were used to map all care providers around the active CHEs. RESULTS: Fever and diarrhoea in the last 3 months occurred most frequently in children under 5; 68% and 41.9%, respectively. For those who sought care, CHE services were used for fever among children under 5, children 5-17 and adults over 18 years of age in 34.7%, 29.9% and 25.1%, respectively. For diarrhoea among children under 5, children 5-17 and adults over 18 years of age, CHE services were used in 22.1%, 19.5% and 7.0%, respectively. For those who did not seek care from a CHE (only), drug shops were most frequently used services for both fever and diarrhoea, followed by health centres or private clinics. Many households used a combination of services, which was possible given the high density and diversity of providers found in the study area. CONCLUSIONS: CHEs play a considerable role in providing care in rural areas where they are active. The high density of informal drug shops and private clinics highlights the need for clarity on the de facto roles played by different providers in both the public and private sector to improve primary healthcare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".